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Muhammad Wali
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Computer Journal
Published by Yayasan YPMMA
ISSN : 29646200     EISSN : 29646219     DOI : https://doi.org/10.58477/cj
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Computer Journal, e-ISSN: 2964-6219 and p-ISSN: 2964-6200 is a free and open-access journal published by the Research Division, YPMMA Institute, Indonesia. Computer Journal is an international, scientific, peer-reviewed, open-access computer science journal, including computer and network architecture and human-computer interaction as its main focus, published six months online. Computers Journal is an international, open access journal which provides an advanced forum for computer sciences. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 92 Documents
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Peningkatan Akurasi Stok Obat melalui Evaluasi Sistem Logistik Menggunakan Root Cause Analysis dan Value Stream Mapping di Rumah Sakit XYZ Ester Novita Sari; Toguy Abella Br Sirait; Anita Christine Sembiring
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.488

Abstract

Hospitals are essential healthcare institutions, making an efficient drug logistics system highly important. This study aimed to identify, analyze, and minimize activities that generated waste or did not provide added value in the pharmacy warehouse of RS. XYZ. Value Stream Mapping was employed to map the actual operational flow and produce a Current Value Stream Map, while Process Activity Mapping was used to classify activities into Value-Added, Non-Value-Added, and Necessary Non-Value-Added categories. Direct observation identified two dominant types of waste, namely Defect and Delay/Waiting, whose root causes were determined through Root Cause Analysis using the 5 Why method and Fishbone Diagram. Several improvement proposals were then developed, including routine Standard Operating Procedure training and additional staff, and illustrated in a Future Value Stream Map representing the improved process flow. Process Cycle Efficiency increased from 2.2% in the Current Value Stream Map to 57.5% in the Future Value Stream Map, an improvement of 55.3%, indicating that the implementation of Value Stream Mapping and Root Cause Analysis effectively reduced waste and improved the efficiency of the drug logistics system in the pharmacy warehouse.
Optimalisasi Perencanaan Persediaan Obat Esensial Menggunakan Analisis ABC (Always Better Control) dan Metode Peramalan untuk Mengurangi Kekosongan Stok di Rumah Sakit XYZ Ika Bana Purba; Anjel Agita Br Ginting; Anita Christine Sembiring
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.489

Abstract

Essential medicines are an integral component of the healthcare system, as their consistent availability is crucial for successful therapy and patient safety. Observations at the Royal Prima Medan Hospital Pharmacy Warehouse identified stockouts, emergency drug purchases (cito), and drug borrowing from other hospitals. This study employed a quantitative descriptive approach to classify essential medicines into Classes A, B, and C using ABC analysis and to forecast demand for Class A medicines over the next six months. Class A, comprising the first three items, accounted for approximately 80% of the total budget and required intensive monitoring. Class B, comprising the next seven items, accounted for approximately 15% of the budget and required periodic monitoring, while Class C, comprising the last ten items, accounted for approximately 5% and required less intensive supervision. The six-month demand forecast for Class A medicines yielded average monthly demands of 6,875.59 units for Ceftriaxone 1 g, 15,789.35 units for Cefixime 200 mg, and 16,971.30 units for Ondansetron 4 mg. These findings provide a basis for improving inventory planning and reducing stockout occurrences.
Implementasi Metode Proof of Reserve pada Sistem Pengiriman Barang untuk Menjaga Integritas Data Dwi Saidina Zulfela Sembiring; Ardiansyah Putra; Alex Zander Siregar; Alfito Surya Pradana; Rico Wijaya Dewantoro
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.490

Abstract

This study implements the Proof of Reserve (PoR) method in a blockchain-based shipment data validation system to support data integrity, authenticity, and reliability. The system was developed on a single server with three CouchDB nodes, consisting of one primary node and two backup nodes. Each shipment record was processed using the SHA-256 hashing algorithm and linked through interconnected prev_hash values to form a cryptographic data chain. This structure enables data modifications to be detected through hash inconsistency across the stored records. Validation was performed using a majority consensus mechanism among the nodes, classifying data as VALID or INVALID based on hash consistency. Simulations conducted in Jupyter Notebook showed that the system could detect data manipulation in both single-node and multi-node scenarios. The system also included an auto-recovery mechanism to restore corrupted or manipulated nodes using data from valid nodes. In addition, the prototype was developed into a web-based interface for real-time shipment tracking number verification. The results indicate that the PoR method can help maintain the consistency, transparency, and accountability of shipment data without requiring a full blockchain implementation.
Evaluasi Penerimaan Generasi Z terhadap Chatbot Islami NURRA Menggunakan Technology Acceptance Model (TAM) Salwa Aulia; Rizki Hikmawan; Raisyal Dimas Prayoga
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.491

Abstract

This study evaluates the acceptance of the Islamic learning chatbot NURRA among Generation Z using the Technology Acceptance Model (TAM). It is motivated by the increasing consumption of digital religious information, which is not always accompanied by substantive understanding, thereby requiring interactive and adaptive learning media. A quantitative survey design was employed involving 30 Generation Z university students who had used NURRA for at least two weeks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that perceived ease of use (PEOU), perceived usefulness (PU), and attitude toward use (ATU) were at high levels, with mean values of 4.342, 4.356, and 4.295, respectively. Structural analysis indicates that PEOU had a significant effect on PU (β = 0.751; p < 0.001) and ATU (β = 0.658; p = 0.003), whereas PU did not significantly affect ATU (β = 0.185; p = 0.420). These findings suggest that ease of use is the dominant factor in shaping user acceptance, while perceived usefulness is not yet strong enough to independently influence attitude toward use. Thus, the initial adoption of NURRA is driven more by ease of interaction than by perceived functional benefits. The findings imply the need to improve content quality so that NURRA becomes more credible and contextual for sustained use.
Analisis Sentimen Ulasan Pengguna inDrive Menggunakan IndoBERT dan Algoritma Genetika pada Klasifikasi K-Nearest Neighbor Muhammad Sigit Nurhafid; Rudiman Rudiman; Taghfirul Azhima Yoga
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.497

Abstract

This study analyzes sentiment in InDrive user reviews from the Google Play Store using IndoBERT, Genetic Algorithm (GA), and K-Nearest Neighbor (KNN). A total of 2,000 reviews were assigned to three sentiment classes: 1,695 negative, 235 positive, and 70 neutral reviews. The pretrained indobenchmark/indobert-base-p1 model was used as a feature extractor by taking the [CLS] representation to produce 768-dimensional embeddings. The dataset was divided using a stratified 80:20 split into 1,600 training and 400 testing samples. The optimal K value was determined through stratified five-fold cross-validation on the training data. GA was applied only to the training set using a population of 30 individuals, 25 generations, a crossover rate of 0.8, a mutation rate of 0.005, and a feature penalty of 0.002. GA selected 250 features, reducing the dimensionality by 67.45%. IndoBERT + KNN correctly classified 365 of 400 test samples, achieving 91.25% accuracy (95% CI: 88.07–93.64%) and a macro F1-score of 66.88%. IndoBERT + GA + KNN correctly classified 362 samples, achieving 90.50% accuracy (95% CI: 87.23–93.00%) and a macro F1-score of 62.71%. Both models exceeded the 84.75% majority-class baseline. However, only three and two of the 14 neutral samples were correctly classified, respectively. GA substantially reduced feature dimensionality but did not improve predictive performance, indicating a trade-off between representation compactness and minority-class classification performance.
Penerapan Teknik Transformasi untuk Meningkatkan Kualitas Citra Penyakit Ganoderma pada Tanaman Kelapa Sawit Menggunakan CNN-Based Enhancement dan Gamma Correction Aditya Aditya; Amanda Amanda; Dini Gustiningsih
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.499

Abstract

Ganoderma disease is one of the most destructive diseases affecting oil palm plants, causing basal stem rot, reduced productivity, plant mortality, and substantial financial losses. This study proposes an image enhancement pipeline for Ganoderma disease identification by combining Gamma Correction and CNN-Based Enhancement. Gamma Correction is applied to improve illumination and pixel intensity, while CNN-Based Enhancement is used to enhance structural and textural details through a deep learning approach. After enhancement, the processed images are classified into three categories: Healthy, Infected, and Initial Infection. The model achieved a test accuracy of 0.7714 with a test loss of 0.4165. For each class, the F1-scores of Healthy, Infected, and Initial Infection were 0.8571, 0.7500, and 0.7200, respectively. The results indicate that image transformation techniques can effectively support Ganoderma disease identification in oil palm plants. By improving image quality prior to classification, the model becomes more stable in emphasizing disease-relevant visual features under diverse lighting conditions. Overall, the proposed enhancement process, which integrates intensity correction with CNN-based reconstruction, offers a promising direction for developing automated detection systems that are more accurate, adaptive, and suitable for field deployment.
Penerapan Ensemble Machine Learning Random Forest dan XGBoost dengan Explainable Artificial Intelligence (XAI) untuk Prediksi Urban Heat Island dan Land Surface Temperature di DKI Jakarta Hertanto Suryoprayogo; Widang Muttaqin; Annisa Desianty
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.501

Abstract

The Urban Heat Island (UHI) effect in tropical urban settings arises from interactions among built surfaces, vegetation, water bodies, and urban energy dynamics. This study modeled Land Surface Temperature (LST) in DKI Jakarta using Random Forest and XGBoost optimized with RandomizedSearchCV and 5-fold cross-validation. The analysis used 5,821 grid points at approximately 300 m resolution and five predictors: road density, NDVI, NDBI, NDWI, and distance to green open space. XGBoost slightly outperformed Random Forest, achieving R² = 0.507 and RMSE = 1.830°C compared with R² = 0.497 and RMSE = 1.849°C, although the difference was not statistically significant (Wilcoxon, p = 0.352). The RF-XGBoost ensemble did not improve performance due to very high residual correlation (r = 0.988) and a theoretical ensemble standard deviation reduction of only ~0.3%. SHAP analysis identified NDBI as the dominant predictor (mean|SHAP| = 0.986), with the strongest interaction between NDBI and road density (0.101). Hyperparameter tuning changed model ranking, statistical significance, and the leading SHAP interaction pair.
Security Analysis on Face Biometric Authentication Under Different AI-Manipulated Face Fathimah Hasanti; Nurul Ilmi; Desi Nurnaningsih
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.503

Abstract

The rapid evolution of Generative Artificial Intelligence (Gen AI) has enabled realistic facial manipulation using AI-generated images, creating serious security threats to face biometric authentication systems. Previous studies have mainly focused on deepfake detection and face recognition improvement, while limited attention has been given to the effects of AI-based facial manipulation on biometric authentication security. This study examines the vulnerability of face biometric authentication to various Gen AI-powered facial manipulation attacks. FaceForensics++ was used to create verification pairs consisting of genuine, impostor, and manipulated faces. ArcFace generated face embeddings, while cosine similarity measured identity similarity between faces. Equal Error Rate (EER) was used to determine the authentication threshold. The threshold was then applied to evaluate DeepFakes, FaceSwap, Face2Face, NeuralTextures, and FaceShifter using False Acceptance Rate (FAR), False Rejection Rate (FRR), EER, and cosine similarity scores. The results indicate different authentication behaviors among manipulation algorithms. Face2Face and NeuralTextures produced higher FAR and similarity scores, indicating greater ability to retain identity information.
Absent or Unretrievable: A Two-Layer Knowledge Management Framework for Retrieval-Critical Product Knowledge in a Grocery E-Commerce AI Shopping Assistant Sinatrio Bimo Wahyudi; Dana Indra Sensuse; Sofian Lusa; Muhammad Hafizh Qurani; Yordan Yasin; Icha Mailinda
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.510

Abstract

AI shopping assistants increasingly employ agent-based retrieval, combining lexical search, structured filtering, and LLM-mediated selection. However, effective retrieval often requires knowledge beyond textual matching. This exploratory single-case study of an Indonesian grocery e-commerce assistant triangulates 279 observations, 52 failure traces, 74 practitioner-reported defects, a schema audit, and four practitioner interviews. The analysis identifies six retrieval-critical product knowledge dimensions and shows that knowledge externalization fails at two distinct representational layers. At the product data-model layer, essential fields—such as allergens, dietary constraints, and age suitability—were absent and remained missing despite architectural changes. At the retrieval-schema layer, available knowledge could not reach candidate sets: structured filters were present in only 29% of calls, and even correctly invoked filters frequently returned empty sets from non-empty pools. Addressing these layer-specific failures, the study proposes a structured knowledge framework based on the knowledge management process cycle. It positions GraphRAG as a future direction to enable hybrid structured retrieval for candidate formation and improve eligibility, substitution, and context-aware ranking.
Predicting Hotel Booking Cancellations Using CatBoost and SHAP: An Explainable AI Approach Based on 2020–2026 Operational Data I Made Sudana; Endang Wahyu Pamungkas
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.511

Abstract

Hotel booking cancellations are a critical problem in hotel revenue management because they can cause operational inefficiencies and financial losses. This study develops an explainable cancellation prediction model using CatBoost integrated with SHAP, based on real-world Property Management System (PMS) operational data from a budget hotel in Central Java, Indonesia. The dataset spans 67 months (October 2020–April 2026) and, after preprocessing and data cleaning, yields 74,826 independent reservation records from 80,110 raw entries. The cancellation rate is extremely low (1.56%), creating a severe class imbalance challenge. Instead of synthetic oversampling, the proposed method applies cost-sensitive learning via CatBoost’s scale_pos_weight, computed from the natural class ratio. Model performance is evaluated using hold-out validation (80% training, 20% testing). The proposed CatBoost model achieves an F1-score of 72.04%, with precision of 87.20% for the cancellation class and an AUC-ROC of 0.86, outperforming Random Forest and XGBoost baselines. SHAP analysis indicates that lead time, deposit type, and arrival month are the most influential features driving cancellation predictions. These findings support early warning decision-making for risk mitigation in hotel operations.

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