Article Per Year (5 Year)
p-Index From 2021 - 2026
3.527
P-Index
This Author published in this journals
All Journal Jurnas Nasional Teknologi dan Sistem Informasi UNEJ e-Proceeding JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informatika Global Jurnal Teknologi Terpadu Progresif: Jurnal Ilmiah Komputer Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Jurnal Generic Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Paradigma SENTRI: Jurnal Riset Ilmiah MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi Applied Information Technology and Computer Science (AICOMS) JISCOMP (Journal of Information System and Computer) INOVTEK Polbeng - Seri Informatika
Abdul Rahman
Universitas Multi Data Palembang
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Pengaruh Image Size pada Penghitung Jumlah Orang Dalam Ruangan Menggunakan Metode YOLOv8 Akbar, Naufal; Rahman, Abdul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.9035

Abstract

Currently, technological advancements have led to the creation of various products that impact company performance. One of the products used in shopping malls is a monitoring application. A temporary closure occurred at one of the stores in a shopping mall due to a surge in visitors caused by a large discount offer. The manual methods currently in use are highly prone to errors. Knowing the number of people in a room is crucial for maintaining service quality. Information about the number of visitors is essential for companies to optimize resource utilization and conduct operational evaluations. This study aims to develop an application for counting the number of people in a room using the YOLOv8 (You Only Look Once) method. Model testing shows that an image size of 640x640 yields a higher mAP compared to the 416x416 size, achieving a precision of 79.6%, a recall of 61.7%, a mAP of 72.3%, and a f1-score of 69.52%. Accuracy testing for counting the number of people shows an accuracy rate of 82.67%.
RANCANG BANGUN ARSITEKTUR ENTERPRISE MENGGUNAKAN TOGAF PADA SISTEM ADMINISTRASI SURAT MENYURAT DI POLDA XYZ Saputra, Joneten; Shandy; Prawinnetou, Wassy; Rahman, Abdul; Wahyu Sudrajat, Antonius
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5641

Abstract

In today's era, advancements in information technology (IT) are driving digital transformation across various sectors, including government institutions such as Polda Xyz. A key element of this transformation is the administrative correspondence system, which must be managed efficiently, structurally, and securely. This study aims to design an enterprise architecture that serves as the foundation for the comprehensive and integrated development of information systems, using the TOGAF (The Open Group Architecture Framework) methodology. This approach addresses challenges such as process inefficiencies, lack of integration between work units, and weak information security. By applying TOGAF through the Architecture Development Method (ADM), the proposed architecture encompasses four core domains: Business Architecture, Application Architecture, Data Architecture, and Technology Architecture. This research adopts a qualitative approach through literature studies, observations, interviews, and data analysis. The results produce an enterprise architecture blueprint and an implementation roadmap for the E-Office system, which are expected to enhance operational efficiency, communication effectiveness, and data security within Polda Xyz.
OPTIMASI RANGKING DOKUMEN DENGAN MODIFIKASI TF-IDF BERBASIS WAKTU PUBLIKASI DAN COSINE SIMILARITY Kamilah, Nyimas Nisrinaa; Aurelia, Reni; Irsyad, Hafiz; Rahman, Abdul
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 3 No 2 (2025): Juni
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jiscomp.v3i2.7172

Abstract

Information Retrieval (IR) tradisional belum mempertimbangkan waktu publikasi dalam menentukan relevansi dokumen. Penelitian ini bertujuan untuk meningkatkan relevansi hasil pencarian dengan memodifikasi metode TF-IDF berbasis waktu publikasi. Metode ini menggabungkan bobot TF-IDF dengan Cosine Similarity untuk mengukur kesamaan antara kueri dan dokumen. Dalam penelitian ini, dataset dievaluasi menggunakan metode yang diusulkan, dengan pengukuran melalui metrix precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa pendekatan ini mencapai precision 0.87, recall 1.00, dan F1-Score 0.93. Berdasarkan hasil evaluasi, penambahan aspek temporal pada metode ini terbukti mampu meningkatkan akurasi IR dalam konteks pencarian informasi terkini
Klasifikasi Kelayakan Ban Sepeda Motor Menggunakan Metode Convolutional Neural Network Rizky, Azzar; Widhiarso, Wijang; Rahman, Abdul
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2917

Abstract

Tires is a primary component in motorcycle that plays a crucial role in driving safety and comfort. Damage to tires, such as wear, cuts, or cracks, can reduce traction, disrupt stability, and increase the risk of traffic accidents. Generally, tire condition inspections are conducted conventionally by technicians who may have limitations in accurately detecting damage. This research aims to develop a tire damage classification system using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture and with transfer learning approach. The dataset used consists of motorcycle tire images categorized into four classes: normal, bald, cutburst, and spotwear. The training process was conducted using a grid search technique to determine the optimal hyperparameter configuration. The best results obtained with a combination of batch size 16, learning rate 0.001, and 43 epochs, yielding a test accuracy of 96.67%, precision of 95%, recall of 95%, and an F1-score of 95%.Keywords: Tire; Convolutional Neural Network; MobileNetV2  AbstrakBan merupakan komponen utama pada kendaraan sepeda motor yang berperan penting dalam keselamatan dan kenyamanan berkendara. Kerusakan pada ban, seperti keausan, sobekan, atau retakan, dapat mengurangi traksi, mengganggu stabilitas, dan meningkatkan risiko kecelakaan lalu lintas. Pemeriksaan kondisi ban secara umum masih dilakukan secara manual oleh teknisi, yang memiliki keterbatasan dalam hal objektivitas dan akurasi. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi kerusakan ban sepeda motor secara otomatis menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2 dengan pendekatan transfer learning. Dataset terdiri dari citra ban sepeda motor yang diklasifikasikan ke dalam empat kelas, yaitu normal, bald, cutburst, dan spotwear. Proses pelatihan dilakukan melalui metode grid search untuk menentukan konfigurasi parameter terbaik. Hasil terbaik diperoleh pada kombinasi hyperparameter dengan batch size 16, learning rate 0.001, dan jumlah epoch 43, menghasilkan akurasi uji sebesar 96,67%, precision 95%, recall 95% dan F1-score 95%.Kata kunci: Ban; Convolutional Neural Network; MobileNetV2
Hybrid Random Forest Regression and Ant Colony Optimization for Delivery Route Optimization Aurelia, Reni; Rahman, Abdul
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1376

Abstract

The transportation of goods in Indonesian cities is increasingly challenged by urbanization, congestion, diverse road characteristics, and environmental factors, reducing the effectiveness of conventional distance-based routing. This study enhances delivery route optimization by integrating travel-time prediction using Random Forest Regression (RFR) with a metaheuristic routing process using Ant Colony Optimization (ACO). Using OpenStreetMap (OSM) data for Palembang, experiments were conducted on five simulated customer locations in Zone 1. Road attributes such as segment length, road type, and estimated speed were used to train the RFR model, whose predicted travel times served as dynamic costs in the ACO heuristic. The RFR model achieved high predictive accuracy (R² = 0.98; MSE = 8.81), and the ACO-based optimization produced an efficient route of 29.58 km with a total travel time of 148 minutes. However, the experiment is limited to a single zone, a small number of customers, and the removal of real traffic variables—where all actual speed variations, congestion levels, and time-dependent traffic conditions were simplified or omitted, causing the model to rely solely on static road attributes. Future work will incorporate real-time traffic data, expand testing to multiple zones, and use larger datasets to improve scalability and operational applicability.
Pendeteksi Kebakaran Menggunakan Metode Transfer Learning dengan Segmentasi Warna dan Citra Rivaldo Rivaldo; Achmad Rizky Zulkarnain; Abdul Rahman
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8374

Abstract

Abstrak - Kebakaran merupakan bencana yang sulit diprediksi dan dapat menyebabkan kerugian besar jika tidak ditangani dengan cepat. Untuk mendeteksi kebakaran lebih awal dan mencegah penyebaran yang lebih luas, penelitian ini mengembangkan sistem pendeteksian berbasis analisis citra. Sistem ini menggabungkan transfer learning untuk klasifikasi awal kebakaran dengan segmentasi warna untuk memastikan lokasi dan cakupan area yang terbakar. Kombinasi metode transfer learning dan segmentasi warna ini memberikan kinerja yang andal dalam mendeteksi kebakaran, menjadikannya solusi efektif untuk sistem pemantauan kebakaran berbasis citra di berbagai lingkungan. Hasil evaluasi menunjukkan bahwa model transfer learning mencapai akurasi sebesar 97,50% pada data pelatihan, 97,98% pada data validasi, dan 99% pada data pengujian. Selain itu, model ini memiliki presisi 100%, recall 96%, dan F1-score 98%. Pada tahap segmentasi warna, program menghasilkan akurasi sebesar 93,5%, presisi 98,6%, recall 94,7%, dan F1-score 96,6%.Kata kunci: Kebakaran, Transfer Learning, Segementasi Warna, Citra Abstract - Fire is a disaster that is difficult to predict and can cause significant damage if not addressed quickly. This study develops a fire detection system based on image analysis to detect fires earlier and prevent further spread. The system combines transfer learning for the initial classification of fires with color segmentation to identify the location and extent of the burned area accurately. Transfer learning and color segmentation provide reliable performance in detecting fires, making it an effective solution for image-based fire monitoring systems in various environments. Evaluation results show that the transfer learning model achieves an accuracy of 97.50% on training data, 97.98% on validation data, and 99% on test data. Furthermore, the model has a precision of 100%, a recall of 96%, and an F1 score of 98%. In the color segmentation phase, the program achieves an accuracy of 93.5%, precision of 98.6%, recall of 94.7%, and an F1-score of 96.6%.Keywords: Fire, Transfer Learning, Color Segmentation, Image
ANALYSING SENSITIVE DATA SECURITY USING TOKENIZATION-BASED DATA MASKING IN THE EXTRACT-TRANSFORM-LOAD (ETL) PROCESS Wayan Dana; Abdul Rahman
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 2 (2026): Research Paper April 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i2.7949

Abstract

Growth of internet users in Indonesia by 8.7% in 2025, reaching 212 million or 74.6% of the total population, has created significant opportunities for the digital economy but also raised concerns regarding data security. Inadequate management of digital data poses risks to public privacy, prompting the Indonesian government to enact Law Number 27 of 2022 on Personal Data Protection (UU PDP) as a legal framework for data protection. Effective data security measures are necessary across various stages, including safeguarding data during the Extract-Transform-Load (ETL) process. This study aims to develop a data masking technique based on tokenization during the transformation stage of the ETL process to enhance the security of sensitive data. The ETL process, which involves extracting data from diverse sources, transforming it into the required format, and loading it into a database, is particularly vulnerable to sensitive data exposure during the transformation stage, especially in unprotected staging environments. Tokenization replaces original data with tokens that hold no intrinsic value, ensuring data confidentiality throughout the transformation and staging phases. The study's findings indicate that tokenization effectively protects sensitive data before it is loaded into the database, while also minimizing the need for duplicate tables or additional storage for masked data. This research contributes practically to supporting the implementation of the UU PDP, strengthening data security in ETL systems, and fostering a secure digital ecosystem in Indonesia.
KLASIFIKASI PENYAKIT DAUN ANGGUR MENGGUNAKAN EFFICIENTNETV2-B0 DENGAN OPTIMIZER ADAMW DAN SGD Dewa Dewa; Abdul Rahman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16847

Abstract

Penyakit yang menyerang daun tanaman anggur dapat berdampak signifikan terhadap penurunan kualitas dan kuantitas hasil produksi, sehingga dibutuhkan sistem deteksi yang mampu bekerja secara cepat dan akurat. Permasalahan yang dikaji dalam penelitian ini adalah penerapan metode klasifikasi otomatis berbasis Convolutional Neural Network (CNN) dengan pendekatan transfer learning untuk mengenali jenis penyakit daun anggur secara optimal, serta menentukan optimizer yang memberikan kinerja terbaik. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi perbandingan kinerja optimizer AdamW dan Stochastic Gradient Descent (SGD) dalam mengklasifikasikan penyakit daun anggur. Metode yang digunakan memanfaatkan arsitektur EfficientNetV2-B0 pretrained yang dilatih menggunakan dataset citra daun anggur yang terdiri dari empat kelas, yaitu Black Rot, ESCA, Healthy, dan Leaf Blight, melalui tahapan feature extraction dan fine-tuning. Kinerja model dievaluasi menggunakan metrik accuracy, precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa kedua optimizer mampu memberikan performa yang baik, namun AdamW menunjukkan hasil yang lebih optimal dengan akurasi pengujian sebesar 97.00%, sementara SGD memperoleh akurasi sebesar 91.00%. Temuan ini menunjukkan bahwa penggunaan EfficientNetV2-B0 dengan optimizer AdamW lebih efektif dalam klasifikasi penyakit daun anggur
KLASIFIKASI TINGKAT KEMATANGAN BUAH JENGKOL MENGGUNKAAN CNN DENGAN ARSITEKTUR MOBILENETV3 Cindy Meilani; Abdul Rahman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16895

Abstract

Buah jengkol merupakan komoditas pertanian bernilai ekonomi tinggi yang memerlukan proses penyortiran berdasarkan tingkat kematangan untuk menjaga kualitas dan nilai jual. Namun, proses penyortiran yang masih dilakukan secara manual sering menimbulkan ketidakkonsistenan dan membutuhkan waktu yang relatif lama. Permasalahan utama yang dihadapi adalah keterbatasan pendekatan klasifikasi tingkat kematangan buah jengkol yang mampu memberikan akurasi tinggi sekaligus efisiensi komputasi. Oleh karena itu, penelitian ini bertujuan untuk membandingkan performa dan efisiensi komputasi arsitektur MobileNetV3-Large dan MobileNetV3-Small dalam mengklasifikasikan tingkat kematangan buah jengkol. Metode yang digunakan adalah computer vision berbasis Convolutional Neural Network (CNN) dengan dataset citra buah jengkol yang dibagi ke dalam tiga kelas, yaitu muda, sedang, dan tua. Evaluasi dilakukan menggunakan metrik akurasi serta analisis efisiensi komputasi. Hasil penelitian menunjukkan bahwa MobileNetV3-Large mampu mencapai akurasi sebesar 97,79%, namun memerlukan sumber daya komputasi yang lebih besar. Sementara itu, MobileNetV3-Small menghasilkan performa terbaik dengan akurasi sebesar 98,53%, serta memiliki kebutuhan komputasi dan waktu inferensi yang lebih rendah. Dengan demikian, MobileNetV3-Small dinilai lebih efektif dan efisien untuk klasifikasi tingkat kematangan buah jengkol
Klasifikasi Penyakit Daun Kentang Berbasis CNN MobileNetV2 dengan Optimasi Randomize Search Jeason Lie; Abdul Rahman; Daniel Udjulawa
TIN: Terapan Informatika Nusantara Vol 6 No 9 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i9.9108

Abstract

Potatoes are a vital food commodity in Indonesia, but their productivity often declines significantly due to attacks by various leaf diseases that inhibit growth. This study aims to build an efficient and accurate automatic classification model for potato leaf diseases using Deep Learning technology. The approach used in this study is the MobileNetV2 Convolutional Neural Network (CNN) architecture based on transfer learning, which is known to have high computational efficiency. To obtain the most optimal model performance, this study applies an automatic hyperparameter tuning strategy using the Randomize Search method and performs robust model validation using the K-Fold Cross Validation technique with 5 folds. In addition, the balanced class weight technique is also applied to overcome the problem of data imbalance in the eight disease classes tested. The experimental results show that the best model configuration is achieved at the 15th iteration of the 5th fold using a combination of RMSprop optimizer parameters, 35 epochs, and a learning rate of 0.001. The final evaluation on independent test data produces an accuracy of 78.45%, a precision of 84.24%, and a recall of 72.94%. The very small difference between the validation accuracy of 79.30% and the test accuracy indicates good generalization ability without overfitting. Although the model achieved excellent results in Alternaria classification, challenges remained in identifying the Fungi class, which has high visual similarity to Phytopthora and Pest. This study concludes that the integration of MobileNetV2 with hyperparameter optimization is capable of effectively classifying potato leaf diseases.
Co-Authors Achmad Rizky Zulkarnain Akbar, Naufal Arifin Arifin Aurelia, Reni Axel Natanael Salim Billy Fernandes Cindy Meilani Daniel Udjulawa Dedy Hermanto Dewa Dewa Eka Puji Widiyanto Hafiz Irsyad Iis Pradesan Inayatulah Inayatulah Jacky Pratama Hasan, Nicolas Jasuandi Wijaya Jeason Lie Johan Halim Kamilah, Nyimas Nisrinaa Kevin Sanjaya Krispinus Pierre Dimas P Molavi Arman Muhammad Sya’ban Nugroho mulyati mulyati Nicolas Jacky Pratama Hasan Nyimas Nisrinaa Kamilah Prawinnetou, Wassy Rayvin Suhartoyo Rivaldo Rivaldo Rizky, Azzar Sachin Sabloak Saputra, Joneten Shandy Shinta Puspasari Steven Tribethran Tasya Angelya Tasya Angelya Tribethran, Steven Valen Julyo Armando Davincy Lin Vicky Salim Wahyu Sudrajat Wayan Dana Widhiarso, Wijang