Jurnal Inotera

Analysis of Potential Hazards at Press Stations Using the Hazard and Operability Study (Hazop) Method at XYZ Palm Oil Mill

Azhar Basyir Rantawi — 2026-07-29

A preventive approach in identifying potential hazards in satiun presses in palm oil mills is very important to prevent work accidents. This study aims to analyze potential hazards at press stations in palm oil mills using the Hazard and Operability Study (HAZOP) method, identify the causes and impacts of each potential hazard found, determine the level of risk based on the results of risk assessments, and prepare appropriate control recommendations as an effort to improve occupational safety and health in the mill environment. The results of the study showed that the level of risk at the press station varied from the Medium category (exposure to hot steam/oil and scratches on the work tools), High (slipping on the floor/slippery stairs and electric shock), to the Extreme category which is very critical in the form of the potential for workers' hands to be pinched on the screw press machineduring cleaning (possibility: 3, severity: 5). As an effort to improve work safety, integrated control recommendations were formulated which include the implementation of strict cleaning SOPs, the implementation of the Lockout/Tagout (LOTO) system on machines and electrical panels, housekeeping improvements with the installation of anti-slip step nosing materials, and the obligation to use standard PPE in a disciplined manner.

A Systematic Review of IoT-Enabled Predictive Drying for Arabica Coffee: Toward a Python-Based Digital Twin Framework for the Gayo Highlands, Aceh Tengah, Indonesia

Muhammad Ihsan — 2026-07-30

Post-harvest drying is the single most decisive determinant of Arabica coffee quality, yet in the Gayo Highlands of Aceh Tengah (950 - 1,650 m.a.s.l.) it remains an experience based, weather dependent operation conducted largely on tarpaulins and open patios. High relative humidity, frequent rainfall, and low ambient temperature routinely prolong drying beyond ten days, produce non uniform final moisture content, and expose parchment to fungal colonisation and ochratoxin risk, causing smallholders to fail the 12.5% maximum moisture threshold of SNI 01-2907-2008 and to forfeit specialty price premiums. This systematic literature review, conducted following PRISMA guidelines on peer reviewed publications from 2019–2026, synthesises three research streams that have so far evolved in isolation: (i) thin layer drying kinetics of parchment coffee, (ii) Internet of Things (IoT) architectures for solar and hybrid dryers, and (iii) digital twin methodology in food and grain drying. The review finds that existing coffee drying IoT deployments are overwhelmingly reactive while validated kinetic models (modified Midilli, Page) capable of such prediction remain confined to laboratory dryers and are never coupled to field sensor streams. Digital twin research in drying has concentrated on grain and fruit, leaving coffee, and highland smallholder contexts in particular, unaddressed. From this synthesis the review derives seven research gaps and proposes SIKOPI-DT, a contextualised five layer Python based digital twin framework in which a modified Midilli kinetic core is continuously reparameterised by low cost IoT sensors (ESP32, SHT31, load cell) to forecast the time to target moisture content and to drive predictive supplementary-heat control under Gayo weather scenarios. Synthesised evidence indicates that such a system can plausibly reduce drying time by 30–50%, cut specific energy consumption by 15–30%, and improve moisture uniformity relative to open-sun practice. A three phase experimental validation roadmap and a smallholder economic feasibility analysis are presented, positioning this review as the theoretical foundation for a physical prototype in Aceh Tengah.

Factors Influencing The Choice Of Transportation Mode Between Tapaktuan And Banda Aceh

Rini — 2026-07-30

Abstract

The development of a region is always accompanied by growth in the transportation sector. Tapaktuan is a developing city. Many residents of Tapaktuan travel to the provincial capital, Banda Aceh. Along the way, they encounter various modes of transportation. The most preferred means of mobility are private cars or public transportation. This study aims to identify the factors influencing the public’s choice of transportation mode. The method used in this study is the ranking method. A total of 80 respondents were surveyed. Data analysis indicates that the variables influencing the choice of transportation options to Banda Aceh include travel time (214), cost/fare (212), vehicle facilities (195), waiting time (181), departure frequency (155), distance (134), and ticketing system (113).

Comparison of the Performance of Machine Learning Classification Algorithms on Phishing URL Detection

Ahmad — 2026-07-30

The development of internet technology has increased the intensity of digital activities, but it has also been followed by an increase in cybersecurity threats, one of which is phishing attacks through malicious URLs. Phishing is a fraudulent method that is carried out by manipulating users through fake websites that resemble official websites to obtain sensitive information, such as usernames, passwords, and financial data. Conventional blacklist-based detection methods are considered less effective in recognizing new phishing URLs that continue to develop dynamically. Therefore, this study aims to analyze and compare the performance of various machine learning and deep learning algorithms in accurately detecting phishing URLs. The dataset used was obtained from Kaggle with a total of 11,054 data points and 31 features that represent the characteristics of phishing and legitimate URLs. The methods used include Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, CatBoost, Extreme Gradient Boosting, and Multilayer Perceptron. The research stages include data preprocessing, exploratory data analysis, data visualization, separation of training and testing data, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results showed that the Gradient Boosting algorithm provided the best performance with an accuracy of 0.974, an F1-score of 0.977, a recall of 0.994, and a precision of 0.986. The results show that the ensemble learning method is able to detect phishing URLs effectively and can be used to improve artificial intelligence-based cybersecurity systems.

Analysis of Discipline and Learning Motivation of Students' Academic Achievement in the Digital Era Using a Linear Regression Model

Medina Fikanti — 2026-07-30

The technological era has drastically changed the educational landscape, providing easier access, but also creating significant challenges. Unlimited access to information can be a significant distraction, potentially reducing the effectiveness of student learning. This negative impact can lead to a decline in discipline and motivation to learn, two crucial internal components for academic success, especially in the context of digital education. Given the importance of discipline and motivation in students' academic adjustment in this modern era, this study aims to empirically test and analyze the impact of discipline and motivation to learn on students' academic performance in the digital era using a Multiple Linear Regression model.

Development of an Interactive Tourism Website for Simeulue Promotion

Sultanu Makrifat — 2026-07-30

Tourism is a strategic sector in supporting regional economic growth, making digital media increasingly important for promotion in the information technology era. Simeulue Regency has significant natural and marine tourism potential; however, its digital promotion media is still not fully optimized. This study aims to develop an interactive tourism website to support tourism promotion and improve public access to tourism information. The system was developed using the Agile Scrum method with an iterative sprint approach. The resulting website provides destination information, interactive maps, and dynamic content management features. Black-box testing results indicate that all core functions operate correctly without significant functional errors. Therefore, the system is considered feasible as a digital tourism promotion platform and can be further enhanced in future development.

The Relationship Between Soil Physics Characteristics with Nilam Production at Pasie Raja Sub-District South Aceh Regency

Harwika — 2026-07-31

Patchouli (Pogostemon cablin) cultivated in Pasie Raja District, South Aceh Regency, is widely recognized for its high-quality essential oil and distinctive aroma. However, despite favourable climatic conditions, patchouli production in this region declined substantially in 2023, reaching only 130 tons, which is considerably lower than the national average. This decline is presumed to be partly associated with soil fertility constraints, particularly soil physical properties. This study aimed to analyse the relationship between selected soil physical characteristics and patchouli productivity in Pasie Raja District. The research was conducted at six patchouli cultivation sites, with soil physical analyses performed at the Materials Testing Laboratory of Politeknik Aceh Selatan. Statistical analysis was carried out using Microsoft Excel to examine the relationships between soil porosity, bulk density, effective soil depth, and patchouli yield. The results indicated that bulk density showed a positive correlation with patchouli productivity, explaining 23.23% of yield variation. Soil porosity also exhibited a positive correlation, accounting for 16.68% of yield variation, while effective soil depth had the strongest association, explaining 36.3% of the variation in patchouli yield. These findings suggest that soil physical characteristics play an important role in patchouli production and may serve as a scientific basis for improving soil management practices to enhance patchouli productivity.

Implementation of MODIS Satellite Image Processing Using SeaDAS Software to Analyze Chlorophyll-a Distribution in Aceh Seawaters Before and After the COVID-19 Pandemic

Mirna Ria Andini — 2026-07-31

Chlorophyll-a is an important oceanographic parameter that reflects the level of primary productivity in the waters and the abundance of phytoplankton as the primary producers in the marine food chain. This study aims to analyze a comparison of chlorophyll-a distribution based on Aqua MODIS and Terra MODIS data in the waters off Aceh during the periods before and after the COVID-19 pandemic.The data used were MODIS Level-3 chlorophyll-a data for the period July–December 2019, representing pre-pandemic conditions, and for the period January–June 2023, representing post-COVID-19 pandemic conditions. Data processing was performed using SeaDAS software version 7.3.2 through the stages of preprocessing, cropping, land masking, visualization, and descriptive statistical extraction, which included the minimum, maximum, and average chlorophyll-a concentrations. Further analysis was conducted using Microsoft Excel to generate graphs and comparison tables for the four data sets. The results of the study show that chlorophyll-a concentrations tend to be higher in coastal areas than in offshore waters, indicating a high supply of nutrients from land via river runoff and water mass mixing processes. The average Aqua MODIS chlorophyll-a concentration decreased from 0.3169 mg/m³ in 2019 to 0.2148 mg/m³ in 2023, while Terra MODIS showed a decrease from 0.3076 mg/m³ to 0.2411 mg/m³ over the same period. The decline in chlorophyll-a concentrations following the COVID-19 pandemic indicates changes in primary productivity in the waters, influenced by oceanographic dynamics such as sea surface temperature, current patterns, monsoon winds, and nutrient distribution. Although there were differences in concentration values between the two sensors due to variations in satellite overpass times and atmospheric conditions at the time of recording, both sensors produced relatively consistent spatial distribution patterns. The results of this study are expected to serve as a reference for the management of marine and fishery resources using remote sensing technology in the waters of Aceh.

Development and Evaluation of an Authentication-Based Access Control Framework for Secure Web-Based Ebook Distribution

Lingga Kurnia Ramadhani — 2026-08-28

The increasing use of digital learning resources has created a growing demand for secure ebook distribution systems. However, many existing ebook platforms remain vulnerable to unauthorized access, credential sharing, and uncontrolled digital content distribution. This study proposes an Authentication-Based Access Control Framework for secure web-based ebook distribution by integrating authentication, role-based authorization, session management, transaction validation, and ebook access protection within a unified security architecture.
The research employed the Waterfall software development model consisting of requirement analysis, system design, implementation, testing, and deployment. The proposed framework was implemented using PHP and MySQL and evaluated through functional and security testing.
The implementation results demonstrate that the developed system successfully manages 153 registered users, 151 ebook transactions, and six published ebook collections while maintaining controlled access to digital content. Functional testing achieved a 100% success rate across authentication, authorization, session validation, and ebook protection scenarios. The session management mechanism effectively prevented concurrent account usage and unauthorized access attempts.
The novelty of this study lies in the integration of authentication, authorization, session validation, and ebook access protection within a dedicated framework specifically designed for ebook distribution. The proposed framework provides a practical, scalable, and cost-effective alternative for educational institutions and digital content providers seeking secure digital resource distribution without relying on complex Digital Rights Management (DRM) infrastructures.

Biomass and Waste Pyrolysis: A Critical Review of Reaction Pathways, Reactor Engineering, Product Quality, Catalytic Upgrading, and Industrial Scale-Up

Yusrizal — 2026-09-10

Pyrolysis is a thermochemical conversion route in which carbonaceous feedstocks are heated under oxygen-deficient or oxygen-free conditions to form a controllable mixture of solid, liquid, and gaseous products. This review critically synthesizes the engineering principles governing biomass and waste pyrolysis, with emphasis on reaction mechanisms, feedstock properties, operating parameters, reactor configurations, product formation, catalytic upgrading, environmental performance, and scale-up challenges. The literature shows that pyrolysis performance is not determined by temperature alone; heating rate, particle size, vapor residence time, pressure, mineral matter, moisture, reactor hydrodynamics, and secondary reactions jointly control product yield and quality. Slow pyrolysis generally favors carbon-rich solid products, whereas fast and intermediate pyrolysis can increase condensable vapors and bio-oil production when heat and vapor residence time are appropriately controlled. Fluidized-bed, circulating-bed, auger, ablative, and fixed-bed reactors each provide different compromises between heat transfer, solids handling, residence-time control, product recovery, and capital cost. Catalytic pyrolysis can reduce oxygenates and increase hydrocarbons or selected chemicals, but catalyst deactivation, coke deposition, contamination, regeneration, and product selectivity remain major barriers. Particular attention is given to oil-palm residues such as empty fruit bunches, palm kernel shells, mesocarp fiber, fronds, and trunks because they are abundant in Indonesia and provide a strong opportunity for decentralized thermochemical valorization. The review concludes that future development should move from yield maximization toward integrated process design, product quality, heat integration, emissions control, techno-economic assessment, life-cycle assessment, and digital process optimization.

Analyzing the Effectiveness of Artificial Intelligence as a Writing Assistant for Enhancing the Academic Writing Skills of Vocational Students

hasbaini kluet — 2026-09-14

This study aims to analyze the effectiveness of using Artificial Intelligence (AI) as a writing assistant to improve the academic writing skills of vocational students. The study employed a mixed methods approach with a quasi-experimental pretest-posttest control group design involving 48 students from Politeknik Aceh Selatan. The research began with a pretest, followed by an intervention in which AI was utilized as a writing assistant in the experimental group, while the control group received conventional writing instruction. After the posttest, observations, interviews, and focus group discussions were conducted to strengthen the quantitative findings. The results showed that the experimental group’s mean score increased from 66.20 to 85.95, which was substantially higher than the control group’s improvement from 66.75 to 75.62. The Wilcoxon Signed-Rank Test and Mann-Whitney U Test yielded a significance value of 0.000 (p < 0.05), indicating statistically significant differences. Interview findings also revealed that 96% of the students perceived AI as highly effective because it helped them generate ideas, organize writing structures, improve grammar, enrich academic vocabulary, and enhance their confidence in academic writing. Therefore, AI has proven to be an effective writing assistant for academic writing instruction while maintaining academic integrity and the originality of scholarly work.

Benchmarking Deep Learning Models for Multi-Class Fruit Quality and Formalin Contamination Detection in Horticultural Fruits

Ahmad Fauzi — 2026-09-14

The adulteration of fresh horticultural produce with formaldehyde (formalin) and the rapid physiological decay of fruit remain persistent food-safety and economic problems within agricultural supply chains. Deep learning offers a non-destructive route to automated fruit-quality screening; however, the literature has largely pursued peak accuracy using progressively heavier network backbones, leaving the computational cost of that accuracy and therefore the feasibility of on-site, embedded deployment insufficiently examined. This study benchmarks three representative visual-recognition paradigms of markedly different scale, namely MobileNetV2 (a depthwise-separable lightweight convolutional neural network), ResNet-50 (a deep residual network), and ViT-B/16 (a patch-based Vision Transformer), on the identical three-class task of distinguishing fresh, rotten, and formalin-mixed fruit. A total of 10,154 images from the public FruitVision dataset were processed under a single fixed preprocessing and training protocol, so that architecture was the only experimental variable. Rather than accuracy alone, performance was assessed through an efficiency lens that normalises accuracy by parameter count and by inference cost (GFLOPs). The lightweight MobileNetV2 attained the highest test accuracy (99.69%) while using approximately one-seventh of the parameters and one-thirteenth of the floating-point operations of ResNet-50, and about one twenty-fifth of the parameters of ViT-B/16; the Transformer recorded the lowest accuracy (99.28%) at the highest cost. These results indicate that, for this medium-scale task, additional architectural capacity yields a negligible accuracy return, and that a compact model can support reliable, safety-critical formalin screening on resource-constrained devices. Per-class reliability and deployment implications, including one residual false negative, are reported without overstatement.

Development of An Automated Pseudocode to Python Translator Application with Three Stage Compilation Visualization For Learning Compilation Techniques

Iis Aisyah — 2026-09-15

Advances in information technology have increased the demand for more effective programming learning approaches, particularly for vocational high school students and early-stage university students who often experience difficulties in understanding algorithmic logic and programming language structures. One of the main challenges lies in the transition from pseudocode, which is conceptual in nature, to actual program code such as Python, where misunderstandings of control structures, data types, and logical flow frequently lead to syntactic and logical errors. This study aims to develop an automatic translator application from pseudocode to Python integrated with a three-stage compilation visualization, namely tokenization, parsing, and code generation. The research adopts a Research and Development (R&D) approach using a prototyping model. The web-based application is developed using the Flask framework and provides features for pseudocode input, interactive visualization of compilation stages, direct execution of generated Python code, and downloading of the conversion results. The system is designed to recognize common pseudocode structures, including IF–ELSE, FOR, WHILE, FUNCTION, INPUT, PRINT, SET, and RETURN, and to automatically handle input data types based on contextual usage. Evaluation is conducted through testing various dynamic pseudocode scenarios with different levels of complexity and input simulations. The expected outcome is a system that enhances learners’ understanding of fundamental programming concepts and compilation processes, reduces syntactic errors, and supports interactive self-directed learning. This research contributes to the development of web-based programming learning tools through the integration of automatic translation and compilation process visualization.