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All Journal Techno.Com: Jurnal Teknologi Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Information Systems and Technology Research Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Sains Informatika Terapan (JSIT) Journal of Computers and Digital Business Wahana Jurnal Penelitian Sistem Informasi International Journal of Economic, Technology and Social Sciences (Injects) Journal of Technology Informatics and Engineering Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Deep Learning, Computer Vision and Digital Image Processing Jurnal Pengabdian Masyarakat Bangsa Agrimas : Jurnal Pengabdian Masyarakat Bidang Pertanian Teknologi : Jurnal Ilmiah Sistem Informasi Journal of Engineering, Electrical and Informatics Journal of Digital Technology and Computer Science
Raden Aris Sugianto
Institut Teknologi Sawit Indonesia, Medan, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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PALMARA: A Location-Aware Digital Intermediation Platform for Transparent Smallholder Palm Oil Distribution Bayu Danuarta; Andi Prayogi; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.961

Abstract

Purpose – Crude Palm Oil (CPO) distribution using tanker trucks faces challenges including delayed reporting, limited visibility, and potential cargo losses due to leakage or human error. This study develops an IoT-based dashboard for real-time monitoring and threshold-triggered leakage alerting during CPO distribution. Methods – A prototype was developed and tested in a controlled laboratory-scale environment. The system integrated an ESP32 microcontroller with a YF-S201 flow sensor, HC-SR04 ultrasonic level sensor, and NEO-6M GPS module. Sensor data were transmitted via MQTT, visualized on a web dashboard, and stored in a Supabase cloud database for historical tracking and operational review. Findings – Testing showed average error rates of 2.48% for the flow sensor and 2.67% for the ultrasonic level sensor. The GPS module captured location data across all test points. The system supported four simulated distribution phases: Loading, Transportation, Unloading, and Completed. During Transportation, a leakage alert was generated when outward flow was detected, confirming that the rule-based alert mechanism operated according to predefined logic. Research Implications – The system offers a prototype framework for improving transparency, traceability, and operational monitoring in CPO logistics. However, field validation using industrial-grade sensors and full-scale tanker truck deployment is required. Originality – This study integrates flow monitoring, tank level measurement, GPS tracking, cloud storage, and rule-based leakage alerting in a single IoT dashboard for CPO tank truck distribution.
Crude Palm Oil Moisture Reduction: Design and Implementation of an Arduino UNO-Based Automated Heating Control System Syem Vicra Lumban Gaol; Andi Prayogi; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.987

Abstract

Purpose – Crude Palm Oil (CPO) quality is strongly influenced by moisture content because excessive water can accelerate hydrolysis, increase free fatty acid formation, promote oxidation, and reduce storage stability. This study aimed to develop and functionally evaluate a low-cost Arduino-based automated heating control prototype to support laboratory-scale CPO moisture-reduction experiments. Methods – This study employed a Research and Development method with a quantitative experimental orientation. The prototype integrated Arduino UNO as the main controller, DHT22 for ambient humidity monitoring, MAX6675 with a Type-K thermocouple for direct CPO temperature measurement, I2C 16×2 LCD for data display, a two-channel 5V relay for heater switching, a 350 W water heater, and LED-buzzer indicators. Functional testing was conducted through observation of sensor readability, LCD display, relay switching, heater response, and integrated system operation. Findings – The results showed that all main components operated according to their intended functions. The system could read humidity and CPO temperature, display real-time data, activate and deactivate the heater through relay control, and provide visual and audible indicators. Research Implications – The prototype provides a functional baseline for automated CPO heating control. However, it does not directly measure actual CPO moisture content because DHT22 only reads ambient humidity. Originality – This study offers a low-cost Arduino-based prototype for supporting CPO heating-control experimentation.
Web-Based Employee Records Governance for Document Traceability and Verification in Plantation Workforce Administration Contexts Natalia Fransiska Ginting; Ritna Wahyuni; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.988

Abstract

Purpose – Employee data administration at PT. Barumun Raya Padang Langkat Binanga Estate has traditionally relied on manual procedures, creating difficulties in information retrieval, document management, verification, and administrative processing. This study aims to develop a web-based Employee Data Management Information System supporting structured, centralized, secure, and efficient personnel administration. Methods – The study employed a Research and Development approach using the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and evaluation. Unified Modeling Language was used to model system processes and database interactions. The application was developed using PHP and MySQL, while functionality was evaluated through Black Box Testing. Findings – The resulting system integrates employee biodata, family information, supporting documents, verification workflows, reporting functions, and role-based access control in a centralized platform. Functional evaluation involved 15 testing scenarios covering authentication, employee data management, document handling, verification, reporting, and user access control. All tested functions produced outputs consistent with the specified requirements, indicating that the system operated correctly. The platform is expected to improve data organization, document control, verification accuracy, and administrative accessibility. Research Implications – The system is limited to employee data and document management and does not include payroll, attendance, recruitment, or performance appraisal modules. Evaluation was also restricted to functional testing. Future studies should therefore assess usability, performance efficiency, reliability, security, and user satisfaction through empirical testing. Originality – This study contributes an integrated personnel administration platform designed for plantation company operations, combining biodata, family records, supporting documents, verification mechanisms, reporting, and role-based access within a single web-based environment.
SIM PSR: A Web-Based Monitoring Platform for Cooperative-Level Smallholder Palm Oil Replanting Program Management Syarifatu Assolehah; Ritna Wahyuni; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.1006

Abstract

Purpose – The Smallholder Palm Oil Replanting Program (PSR) seeks to improve the productivity of smallholder plantations by replacing aging oil palm trees with certified superior seedlings. However, monitoring at the cooperative level is often hindered by manual record-keeping and fragmented reporting, reducing the efficiency of supervising program implementation. This study aims to develop and evaluate a web-based monitoring information system for the PSR program at Koperasi Tani Makmur Berjaya, Air Hitam Village, North Labuhanbatu Regency. Methods – The system was developed using the Waterfall methodology with UML-based system modeling and implemented using PHP and MySQL. It supports three user roles administrator, field officer, and participant with permissions tailored to operational responsibilities. Functional performance was evaluated through developer-conducted Black Box Testing across 25 functional test scenarios involving administrator, field officer, and participant roles. Findings – Functional testing demonstrated that the proposed system supports the management of participant information, land data, documents, stage-based monitoring records, and PDF report generation under controlled testing conditions. It supports monitoring for 116 participants across approximately 480 hectares in five villages. Testing confirmed that all functions operated as intended without identified functional failures. Research Implications – The proposed system provides a centralized digital platform for cooperative-level PSR monitoring. However, the findings are limited to a single cooperative implementation and developer-conducted functional testing, without independent usability evaluation, independent or advanced security assessment, or long-term operational validation beyond the basic internal security checks conducted in this study. Originality – This research presents a web-based monitoring system specifically designed for cooperative-level PSR management in Indonesia, integrating role-based access control, stage-oriented progress monitoring, and automated report generation within a unified platform.
RT-DETR-Based Computer Vision System for Real-Time Detection and Classification of Oil Palm Fruit Maturity Levels in Plantations Nur Hafiqah Rambe; Ratu Mutiara Siregar; Raden Aris Sugianto
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1281

Abstract

Purpose – This study aimed to develop an automated oil palm fruit maturity level detection system using the real-time detection transformer (RT-DETR) algorithm to overcome the limitations of conventional visual inspection methods, which are often subjective and inconsistent. This study evaluated the effectiveness of the RT-DETR in detecting and classifying oil palm fruit maturity levels to support quality control processes in plantation operations.Method – A computer vision-based approach was implemented using the RT-DETR-L object detection model. The dataset consisted of 14,620 annotated oil palm fruit images categorized into four maturity levels: unripe, underripe, ripe, and overripe. The research process included data collection, image annotation, preprocessing, model training, and evaluation of the model. The model performance was assessed using precision, recall, mean Average Precision (mAP@50), and inference speed metrics.Findings – The experimental results show that the RT-DETR-L model achieved a precision of 93.2%, 95.6%, and mAP@50 of 96.9%, respectively. The model successfully detected and classified oil palm fruit maturity levels across all categories with high accuracy. Furthermore, the model achieved an inference time of 25–28 ms per image and a processing speed of 10–14 FPS on an NVIDIA RTX 3050 4GB GPU, demonstrating its capability for real-time applications.Research Implications – The findings indicate that RT-DETR-L can improve the efficiency, consistency, and accuracy of oil palm fruit sorting and quality control processes. However, this study was limited to the available datasets and testing scenarios used. Future research should evaluate the model under diverse environmental conditions, lighting variations, and field deployment settings to improve its generalizability and robustness.Originality – Unlike previous studies that primarily employed CNN-based detectors or focused on binary maturity classification, this study investigated the application of a transformer-based RT-DETR-L architecture for detecting four oil palm fruit maturity categories. The results demonstrate that RT-DETR-L can provide high detection accuracy while maintaining real-time performance in smart agriculture applications.
Laravel Dashboard for Immature Oil Palm (TBM III) Monitoring Using XYZ Tiles and Large Language Models Maghfirah; Ritna Wahyuni; Raden Aris Sugianto
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1449

Abstract

Purpose – This research addresses the strategic urgency of digitalizing plantation monitoring to achieve precision agriculture at PTPN IV Regional I. The monitoring of Immature Plants (TBM) III currently relies on fragmented manual spreadsheets, leading to data redundancy and delayed analysis.Methods – A web-based data visualization dashboard was developed using the Laravel framework, integrating Geographic Information Systems (GIS) with Static Raster Tiling (XYZ Tiles) to optimize high-resolution map rendering. The system incorporates Large Language Model (LLM) API integration (Gemini 1.5 Flash and Llama 3) for prescriptive analytics, transforming biometric growth data into automated maintenance recommendations through prompt engineering.Findings – Results indicate that the system achieves significant workflow simplification by transforming the fragmented, multi-stage manual reporting pipeline into an automated, single-step data ingestion process, successfully reducing administrative touchpoints. The Static Raster Tiling (XYZ Tiles) technique successfully reduced high-resolution orthophoto rendering latency from over 12,000 ms to an average of 180 ms. Validation using Fleiss' Kappa statistics yielded a score of 0.8105, categorized as "Almost Perfect Agreement," confirming that the AI-generated recommendations are highly consistent with expert agronomic standards. Research implications – This system provides a comprehensive managerial evaluation tool, bridging the gap between raw field data and strategic decision-making in oil palm management.Originality – The integration of spatial optimization and prescriptive AI analytics offers a novel approach compared to existing descriptive-only monitoring platforms.
Development of an Integrated Web-Based Palm Oil Production Monitoring Dashboard Using Laravel at PT Paluta Inti Sawit Aditya Pratama; Raden Aris Sugianto; Andi Prayogi
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1470

Abstract

Purpose – Data management at PT Paluta Inti Sawit (PT PIS) currently faces efficiency constraints due to fragmented operational data for Fresh Fruit Bunches (FFB), Crude Palm Oil (CPO), and Kernel (PK) stored in scattered spreadsheets. This study aims to design and build an integrated web-based dashboard to centralize monitoring and automate production reporting.Methods – The research employs an applied system development and functional validation design following the Waterfall System Development Life Cycle (SDLC). The system was developed using the Laravel framework and MySQL database, with ApexCharts for near-real-time interactive visualization after data ingestion.Findings – The developed dashboard successfully integrates multi-departmental production data into a single source of truth. Key features include an automated Excel/CSV parser with conflict resolution (skip/overwrite), a two-level role-based verification system (Staff and Manager), and near-real-time KPI tracking for Oil Extraction Rate (OER) and Kernel Extraction Rate (KER) upon data approval. Research implications – This system accelerates the daily reporting cycle and supports data integrity through a digital audit trail, assisting rapid, data-driven managerial decisions.Originality – Unlike previous studies that focus on mobile-only reporting, this system provides a mill-level integrated prototype for synchronized FFB reception and processing yield analytics, minimizing manual recapitulation errors.
Perbandingan Support Vector Regression dan Long Short-Term Memory untuk Prediksi Harga Crude Palm Oil Berjangka Khoirun Nisa Harahap; Ratu Mutiara Siregar; Raden Aris Sugianto
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.996

Abstract

Indonesia sebagai produsen Crude Palm Oil (CPO) terbesar dunia memerlukan sistem prediksi harga yang akurat untuk mendukung pengambilan keputusan strategis. Penelitian ini membandingkan kinerja algoritma Support Vector Regression (SVR) dan Long Short-Term Memory (LSTM) dalam memprediksi harga CPO berjangka Bursa Malaysia menggunakan 1.426 data harian dari Investing.com periode 2 Januari 2020 hingga 30 Desember 2025. Data diproses melalui pembersihan, normalisasi Min-Max, dan pembentukan sekuens dengan lookback 30, lalu dibagi secara kronologis dengan rasio 80:20. SVR menggunakan kernel Radial Basis Function (RBF) dengan penyetelan parameter melalui GridSearchCV, sedangkan LSTM dibangun dengan arsitektur dua lapis dan Early Stopping. Evaluasi dengan RMSE dan MAPE menunjukkan kedua model mencapai akurasi sangat baik dengan MAPE di bawah 2% pada data uji. SVR memperoleh MAPE 1,29% dan RMSE 76,17 MYR/ton, sedangkan LSTM memperoleh MAPE 1,32% dan RMSE 75,22 MYR/ton. Perbedaan antar model sangat kecil sehingga tidak dapat diklaim signifikan secara statistik tanpa uji formal. Temuan ini mengindikasikan bahwa SVR dan LSTM memiliki kapabilitas setara untuk prediksi harga CPO jangka pendek berbasis data historis, dengan keterbatasan yang sama dalam menghadapi guncangan eksternal yang tidak terepresentasi pada variabel input.
Application of the Mamdani Fuzzy Logic Method in an Expert System for Determining Oil Palm Fertilizer Dosage Based on Soil Condition and Plant Age Rizki syawaluddin; Raden aris sugianto; Ritna wahyuni
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fertilization accounts for 40-60% of total oil palm maintenance costs, yet smallholder farmers who manage roughly 41% of Indonesia's oil palm area still determine dosages from habit and visual estimation rather than from measured soil condition or plant age, a gap linked in field surveys to dosage errors of 25-35% relative to PPKS-recommended values. Prior decision-support tools, including web-based lookup tables and static recommendation charts, lacked dynamic input handling, did not model the inherent uncertainty in soil nutrient data, and were validated only internally without independent field verification. To close this gap, this study designed, implemented, and empirically validated SawIT PalmExpert, a Mamdani Fuzzy Logic-based expert system that recommends macro-fertilizer dosages (Urea, TSP/SP-36, KCl/MOP, Kieserite/Dolomite) from five inputs soil pH, Nitrogen (N), Phosphorus (P), and Potassium (K) obtained from accredited laboratory analysis (pH H2O 1:5; Kjeldahl N; Bray-1 P; NH4OAc K), together with plant age. The specific contribution of this research is threefold: (1) the first documented web-based expert system to integrate soil chemical status and plant age simultaneously as continuous fuzzy inputs for oil palm fertilizer dosing, rather than treating them as independent lookup criteria; (2) a 24-rule Mamdani knowledge base explicitly calibrated against PPKS agronomic standards through structured expert interviews and three rounds of rule validation, rather than derived from generic literature; and (3) a multi-dimensional empirical validation combining functional, accuracy, usability, and field simulation testing on a single deployed system. Concretely, black-box functionality testing across 10 scenarios all passed; fuzzy output accuracy testing on 15 cases against PPKS references yielded 100% conformity within a +/-10% tolerance (maximum deviation 2.9%, confined to fuzzy-set transition zones); usability testing with 10 purposively sampled respondents (6 smallholder farmers, 4 extension workers), using a validated instrument (content validity index 0.88; Cronbach's alpha 0.84), produced an overall score of 4.24/5.0 ("Very Good") across five dimensions ease of use, information clarity, response speed, recommendation relevance, and overall satisfaction; and field simulation testing on three representative soil scenarios received full agronomist approval. These results demonstrate that a laboratory-calibrated Mamdani fuzzy expert system can deliver both algorithmic accuracy and practical usability, positioning SawIT PalmExpert as a scientifically grounded, field-ready decision-support alternative to experience-based fertilization for smallholder oil palm farmers.
Pengembangan Model Kesiapan Transformasi Digital Rumah Sakit Berbasis Artificial Intelligence Menggunakan Human–Technology–Organization Framework Ritna Wahyuni; Raden Aris Sugianto
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1180

Abstract

Kemajuan pesat Kecerdasan Buatan (AI) telah mendorong organisasi layanan kesehatan untuk mempercepat inisiatif transformasi digital guna meningkatkan kualitas layanan, efisiensi operasional, dan daya saing organisasi. Namun, banyak rumah sakit masih menghadapi tantangan dalam mengimplementasikan transformasi digital berbasis AI karena tingkat kesiapan yang bervariasi di berbagai dimensi manusia, teknologi, dan organisasi. Studi ini bertujuan untuk mengembangkan model komprehensif kesiapan transformasi digital di rumah sakit dengan mengintegrasikan kerangka kerja Manusia-Teknologi-Organisasi (HTO) dengan kemampuan Kecerdasan Buatan. Model yang diusulkan meneliti pengaruh kompetensi digital, literasi AI, infrastruktur teknologi, kesiapan data, dukungan organisasi, dan budaya digital terhadap kesiapan transformasi digital. Pendekatan penelitian kuantitatif akan digunakan dengan menggunakan data survei yang dikumpulkan dari para profesional kesehatan, administrator rumah sakit, dan personel teknologi informasi. Pemodelan Persamaan Struktural (SEM) akan digunakan untuk mengevaluasi hubungan antar variabel dan memvalidasi model yang diusulkan. Temuan yang diharapkan diharapkan dapat mengidentifikasi penentu utama keberhasilan transformasi digital berbasis AI di rumah sakit dan menyediakan kerangka kerja komprehensif untuk menilai kesiapan organisasi. Studi ini berkontribusi pada pengembangan teori transformasi digital di bidang kesehatan dan menawarkan panduan praktis bagi manajemen rumah sakit dalam merencanakan dan mengimplementasikan strategi transformasi berbasis AI untuk meningkatkan kinerja layanan kesehatan dan keberlanjutan organisasi.
Co-Authors Aditya Pratama Ahmad Billy Sutanto Ananda, Aulia Andi Prayogi Andi Prayogi Andi Prayogi Andi Prayogi Anton Purnama Arif Pratama Bayu Danuarta Bintang Permata Hati Simanjuntak Delyana R. Pulungan Dian Maya Sari Dina Arfianti Saragih Elisa Khairanis Fahrizal Fahrizal Febriani Putri Wulandari Fitri Annisa Alfi Siregar Hafiz Rizqa Pratama Harahap, Rizky Musarif Hasanal Fachri Satia Simbolon INDAH SARI SIAGIAN ismatul khoiriyah Khairul Saleh Khoirun Nisa Harahap M. Feril Novial M. Ilham Saputra Maghfirah Makharani Sari Ginting Marbun, Yohanes Jaya Mentari Marpaung, Ozi Rizal Miftahul Jannah Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Muhammad Gunawan Mutiara Cahyani Najib, Zaki Abdullah Natalia Fransiska Ginting Navea, Roy Francis Nur Hafiqah Rambe Prayogi, Andi Putra Jaya Sinulingga Rahmad Dian Ratu Mutiara Siregar Ratu Mutiara Siregar Ridho Agustiawan Rangkuti Rina Maharany Ritna Wahyuni Ritonga, Rizky Rastra Fahrezi Rizki syawaluddin Roby Ade Wijaya Roslina, Roslina Rudi Gunawan Septia Dwi Pratiwi Sianturi, Paris Simbolon, Hasanal Fachri Satia Sitanggang, Elprida Kristina Sri Lestari Rahayu Syarifatu Assolehah Syem Vicra Lumban Gaol Tifany Zia Aznur Triansyah, Angga Wulan dari Zakarias Situmorang Zia