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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 535 Documents
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Analysis of Early Detection of Automatic Weather Station Sensor Failures: A Comparative Study of Supervised Deep Neural Networks and Unsupervised Autoencoders Hasbullah Zuhri Hasibuan; Sudarno Wiharjo; Yan Mitha Djaksana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7593

Abstract

Automatic Weather Stations (AWS) continuously collect and record meteorological data in real time and support weather information services. The reliability of AWS observations depends on sensor performance, as sensor failures, equipment degradation, and communication disturbances may produce anomalous data and reduce data quality. This study aims to develop an early-detection approach for AWS sensor anomalies using a Deep Neural Network (DNN) and an Autoencoder, compare their performance, and identify the sensors most frequently associated with anomalies. The dataset consists of 385,237 observations collected from the AWS Ancol station in North Jakarta from January to September 2025, covering nine meteorological and oceanographic parameters. The study involved data preprocessing, Min-Max normalization, model training, and evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. The DNN achieved 99.6% accuracy, 0.922 precision, 0.996 recall, 0.958 F1-score, and 1.000 AUC. The Autoencoder achieved 95.8% accuracy, 0.578 precision, 0.023 recall, 0.043 F1-score, and 0.584 AUC. The AUC of 1.000 obtained by the DNN should be interpreted within the characteristics of the labeled dataset used in this study and should not be considered evidence of perfect generalization. Sensor analysis identified wind direction as the parameter most frequently associated with anomalies, contributing 46.67% of the detected anomalies in the DNN and 66.02% in the Autoencoder. These findings indicate that the supervised DNN performed better than the Autoencoder for anomaly detection on the labeled AWS dataset used in this study.
Dijkstra Algorithm-Based Shortest Path Optimization for Multi-Destination Tourism Routes in Samosir Regency: A Google Maps-Driven Case Study Yesy Simanjuntak; Fadillah Amanah; Suvriadi Panggabean
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7604

Abstract

Samosir Regency offers various natural and cultural tourism destinations distributed across several sub-districts. The distribution of these destinations and their road connections can make it difficult for visitors to determine efficient travel routes. This study aimed to determine the shortest route from Tano Ponggol to Hadabuan Nasogo Waterfall using a weighted graph approach and Dijkstra's algorithm based on distance data obtained from Google Maps. Nine tourist attractions were modeled as vertices and thirteen connecting routes as weighted edges representing travel distances for four-wheeled vehicles. Distance data were collected from Google Maps in April 2025 and processed using Dijkstra's algorithm. The results showed that the shortest route was A→B→D→E→H→I, with a total distance of 100.1 km and six of the nine attractions included in the route. Compared with the sequential baseline route that passes through all nine destinations (A→B→C→D→E→F→G→H→I), the shortest route reduced the travel distance by 31.6 km (24%) and the estimated travel time by 1 hour and 3 minutes (27%). However, the shorter route bypassed three attractions (C, F, and G), showing that a shortest-path approach does not ensure coverage of all tourism destinations. Dijkstra's algorithm is therefore suitable for determining the shortest route between a selected source and destination, while tourists who intend to visit multiple destinations may require a multi-stop optimization approach, such as the Traveling Salesman Problem. The findings provide a route recommendation for tourism travel in Samosir Regency and illustrate the need to select an optimization method according to the intended travel objective.
Implementation of SOSIOCARE as a Website-Based Student Administrative Service System to Support Digital Academic Service Transformation Moh. Pebrianto; Seli Septiana Pratiwi; Sepvyolla Putri Agustin; Zulfa Meutia Putri; Annisa Nur Cahyaningrum
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7613

Abstract

Administrative service management in higher education institutions increasingly requires digital transformation to meet students’ needs for accessible, transparent, and efficient services. This study aims to describe the implementation of SOSIOCARE as a website-based student administrative service system at the Department of Sociology, Faculty of Social Sciences, Universitas Negeri Malang, and to examine its role in supporting digital academic service transformation. A qualitative descriptive approach was employed, with data collected through in-depth interviews with five informants consisting of three students, one administrative staff member, and one department head. Data were analyzed using the interactive model proposed by Miles, Huberman, and Saldaña, which includes data reduction, data display, and conclusion drawing and verification. The findings indicate four main outcomes. First, SOSIOCARE provides easier access to administrative services, with the three student informants reporting a transition from manual to system-based document status tracking during the initial period of implementation. Second, the service monitoring feature provides clearer status information through six stages of service tracking, allowing students to monitor their requests without repeatedly contacting administrative staff. Third, the system reduces repetitive inquiries regarding service status and helps administrative staff manage service data in a more organized manner. Fourth, the department head views SOSIOCARE as a positive step toward digital transformation of academic services and emphasizes institutional commitment as important for its continued development. Overall, the findings indicate that SOSIOCARE functions not only as a platform for submitting administrative requests but also as an integrated system for monitoring and managing student administrative services at the departmental level.
Development of a Google Apps Script-Based Transaction System with Business Intelligence for Sales Trend Analysis at Dapoer Mba Nina MSME Muhammad Umar Hafidz; Dadang Iskandar Mulyana; Mesra Betty Yel
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7614

Abstract

Dapoer Mba Nina is a culinary micro, small, and medium-sized enterprise (MSME) that still relies on manual transaction recording using a cash book, which creates risks of recording errors, difficulties in monitoring sales trends, and challenges in making data-driven business decisions. This study aims to design and develop a web-based transaction system using Google Apps Script integrated with a Business Intelligence dashboard based on Chart.js to analyze sales trends at Dapoer Mba Nina. The study employed an applied research approach using the Waterfall system development model, which consists of requirements analysis, system design, implementation, testing, and evaluation. The developed system uses Google Apps Script for data processing and the web application, Google Sheets as a cloud-based data storage medium, and Chart.js for visualizing sales data on the dashboard. System testing was conducted using Black Box Testing to validate system functionality and User Acceptance Testing (UAT) to assess user acceptance and the suitability of the system for operational needs. The implementation results show that the system can automate sales transaction recording, generate daily, weekly, and monthly sales reports, and display sales trends and best-selling products through an interactive dashboard. The Black Box Testing results show that all tested scenarios operated as designed, while User Acceptance Testing achieved a score of 96%, categorized as very good. The developed system can help improve transaction data management efficiency and support data-driven business decision-making at Dapoer Mba Nina.
Optimization of Skin Disease Image Segmentation: A Hybrid Approach Using HE-LAB Color Space with Canny and Otsu Methods Andi Saidah; Tundo Tundo; I Made Agus Oka Gunawan; Roy Kasiono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7620

Abstract

Skin lesion detection through image analysis requires accurate segmentation methods to distinguish lesion regions from surrounding healthy skin. This study evaluates a hybrid approach that combines contrast enhancement using Histogram Equalization (HE) and HE applied to the Lightness (L) channel in the LAB color space (HE+LAB) with two segmentation methods, namely Canny edge detection and Otsu thresholding. The segmentation performance was evaluated at three image resolutions: 64 × 64, 128 × 128, and 256 × 256 pixels, using Accuracy, Precision, Recall, F1-Score, and Intersection over Union (IoU). The experimental results show that the HE+LAB-Otsu combination consistently achieves higher performance than the other evaluated combinations across the three resolutions. At 128 × 128 pixels, HE+LAB-Otsu achieves an Accuracy of 0.7170, F1-Score of 0.7407, and IoU of 0.5882. Canny-based segmentation generally produces lower scores, particularly for Recall and IoU, indicating difficulties in extracting complete lesion regions. The use of HE in the LAB color space improves lesion contrast while preserving the chromatic components of the image, resulting in better segmentation performance than standard HE in the evaluated experiments. The results indicate that the combination of HE+LAB and Otsu thresholding is a promising conventional approach for skin lesion image segmentation. However, further evaluation using additional datasets, statistical testing, and comparisons with modern deep learning segmentation methods is required to assess its generalizability and applicability to automated dermatological image analysis.
Analysis of Factors Influencing Artificial Intelligence Adoption Among MSMEs in Batam City Using the TOE and TAM Approaches Daniel Daniel; Heru Wijayanto Aripradono; Surya Tjahyadi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7631

Abstract

Artificial intelligence (AI) has considerable potential to enhance operational competitiveness, but its adoption among Micro, Small, and Medium Enterprises (MSMEs) remains constrained by structural and perceptual barriers. This study examines the factors influencing AI adoption intention among MSMEs in Batam City using the Technology-Organization-Environment (TOE) framework and Technology Acceptance Model (TAM), with Diffusion of Innovation (DOI) providing additional theoretical support. A sequential explanatory mixed-methods design was employed. Quantitative data were collected from 270 MSME practitioners and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The measurement model met the criteria for convergent validity, discriminant validity, and reliability after the removal of the Environmental Pressure construct due to discriminant validity issues. The structural model showed that all six hypotheses retained in the final model were supported. Technology Readiness and Organizational Support had significant positive effects on Perceived Usefulness and Perceived Ease of Use, while Perceived Usefulness was the strongest direct predictor of AI adoption intention. Qualitative interviews with five MSME practitioners further supported the quantitative findings by indicating that practical operational benefits and internal readiness were important considerations in AI adoption. Based on these findings, MSME managers and policymakers in Batam City should emphasize the practical operational benefits of AI and strengthen digital infrastructure and organizational readiness to support adoption.
Implementation of the Certainty Factor Method in an Expert System for Diagnosing Nervous System Diseases Aldio Tri Bangkit Sanjaya; Dwi Hartanti; Ridwan Dwi Irawan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7658

Abstract

Neurological disorders present diagnostic challenges because of overlapping symptoms and limited access to specialist services in some healthcare settings. This study develops and evaluates a web-based expert system for supporting the preliminary diagnosis of five conditions—Stroke, Vertigo, Migraine, Low Back Pain, and Arthritis—using the Certainty Factor (CF) method. The CF approach incorporates Measure of Belief (MB) and Measure of Disbelief (MD) to represent uncertainty in the relationship between symptoms and diseases. The study adopts a design and development research (DDR) approach, with system development supported by an expert system development life cycle. The diagnostic performance was evaluated using 12 test cases, with the system results compared with assessments from an expert neurologist. The system correctly matched the expert assessment in 11 of 12 cases, resulting in a diagnostic agreement rate of 91.67%. One mismatch occurred in Case 2, in which the system identified Vertigo while the expert assessment indicated early-stage Stroke, reflecting the difficulty of distinguishing conditions with overlapping symptoms using the defined rules. Black-box testing showed that all tested functional scenarios passed, resulting in a 100% functional test pass rate. The developed system can support preliminary neurological disease identification by providing diagnosis results accompanied by Certainty Factor values.
Design of a Student Thesis Topic Recommendation System Using the K-Nearest Neighbor Algorithm Abiil Maazzalla; Eza Budi Perkasa
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7659

Abstract

Determining the right thesis topic is a challenge for students because it is often not aligned with their abilities, interests, and experience, resulting in a less than optimal research process. This study aims to design a data-based thesis topic recommendation system using the K-Nearest Neighbor (KNN) algorithm. Data were collected through a questionnaire that measures three main aspects of students, namely abilities, interests, and experience in the fields of programming, web development, system security, and computer networks. Qualitative data were then converted into a numeric format using a Likert scale and binary values ​​to be processed as a classification dataset. The KNN algorithm was implemented with Euclidean Distance calculations and a majority voting mechanism using K = 3 and K = 5 values. System testing with a training and test data division ratio of 80:20 resulted in an accuracy rate of 80%. These results indicate that the system is able to provide relevant and objective topic recommendations according to student profiles. This study proves that a data-driven approach and the KNN algorithm can be an effective solution to support systematic academic decision-making.
Usability Evaluation of BMKG Pontianak Maritime Info Portal Using ISO 9126 Standard and USE Questionnaire Prima Surya Hadikusumah; Rian Vernendy; Antik Avelia Carolin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7681

Abstract

The high risk of fatalities from maritime accidents in West Kalimantan waters due to extreme weather conditions requires a reliable meteorological information dissemination system for coastal communities. The BMKG Maritime Meteorology Station Pontianak launched the Maritime Info Portal as a digital safety platform, but its usability for non-expert users has not yet been formally evaluated. This study aims to evaluate the usability of the portal based on the ISO 9126 standard, focusing on four characteristics: Understandability, Operability, Learnability, and Attractiveness, using the USE Questionnaire as the measurement instrument. This study employed a descriptive quantitative method with purposive sampling involving 30 valid respondents consisting of traditional fishermen, IT academics, and general users. Statistical testing using IBM SPSS showed that all 13 questionnaire items met the validity criteria, while the instrument demonstrated high internal consistency with a Cronbach's Alpha value of 0.924. The results showed that all four usability characteristics were rated Good, with Attractiveness obtaining the highest score at 80.44%, followed by Operability at 76.22%, Learnability at 75.78%, and Understandability at 70.00%. Field observations also indicated that some traditional fishermen experienced difficulties in interpreting formal meteorological terms, particularly wind speed units such as knots. Based on these findings, practical recommendations include the use of localized terminology for fishermen, automated unit conversion, and mobile web optimization for low-bandwidth coastal areas.
Accuracy Testing of Laptop Damage Diagnosis Using Forward Chaining and Certainty Factor Algorithms Muhammad Farhan; Eza Budi Perkasa
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7726

Abstract

Laptops are widely used for educational and professional activities, but non-technical users often have difficulty identifying hardware problems, which can delay repairs and increase costs. This study developed and evaluated a web-based expert system for diagnosing laptop hardware damage using the Forward Chaining (FC) algorithm and the Certainty Factor (CF) method. The knowledge base consisted of 30 symptoms and 11 hardware damage categories derived from observations and interviews with professional laptop technicians. Accuracy testing was conducted using 20 test cases, consisting of 16 general user questionnaire cases and 4 technician-verified real-world cases. The diagnostic results generated by the system were compared with technician diagnoses as the reference. The hybrid FC-CF approach correctly diagnosed 15 of 20 cases, resulting in an accuracy of 75%, while standalone Forward Chaining correctly diagnosed 5 cases, resulting in an accuracy of 25%. The results indicate that the use of Certainty Factor allows the system to produce diagnoses when user-provided symptoms are incomplete or uncertain. The remaining incorrect diagnoses were associated with user subjectivity in assigning confidence levels and overlapping symptoms among hardware damage categories. Based on the test results, the developed system can be used as a preliminary diagnostic aid for laptop hardware problems and should not be considered a replacement for professional technician assessment.

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