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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 387 Documents
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Monitoring Information System to Ensure Completion of Medical Procedure Informed Consent Forms at Hospital X Shinta Yuspita; Yuyun Yunengsih; Falaah Abdussalaam
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5263

Abstract

Medical action consent forms are known to be legal documents in the delivery of healthcare services. However, when they are not well completed, it affects the quality of services and the accountability of an institution. Recurring problems in documentation processes include incomplete data entry, late submissions, and substandard recording; all these increase risks for medical errors and administrative complications. A web-based information system was developed to address these systematic deficiencies using Rapid Application Development (RAD) methodology. Field observations and structured interviews with healthcare personnel were used as research tools in the design of this system. The development process continued through four iterative phases: requirements planning, user-centered design, system construction, and deployment evaluation. This platform comprises automated validation protocols that prevent incomplete submissions from being accepted into the database until all required fields have been completed by the user; real-time alert mechanisms for missing data fields within a submission; and streamlined interfaces optimized for clinical workflows. Functional testing shows statistically significant improvements in documentation efficiency and completion rates among medical staff documented by the system compared to those not documented by it. The system permits real-time documentation of procedures with lower error margins compared to existing methods plus shorter processing times than existing methods. This study found that RAD-based solutions present feasible approaches toward improving accuracy and speed in medical consent documentation while simultaneously achieving legal compliance and patient safety goals within hospitals. The platform uses technology-driven quality assurance mechanisms to solve longstanding challenges in documentation
Implementation of a Chatbot Using the Waterfall Method to Improve Helpdesk Service Efficiency at IT Consulting Companies Sri Lestari; Eka Putri Aprillia; Raisah Fajri Aula
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5207

Abstract

PT XYZ is a company engaged in information and communication technology services, supporting customers' digital transformation. The effectiveness of helpdesk services plays a crucial role in maintaining operations and fostering customer relationships. However, the issue reporting process is still handled manually through platforms such as WhatsApp and email, causing several problems, including inefficient ticket management, delays in ticket number assignment, and limited historical data. This study developed a chatbot based on Microsoft Copilot Studio to automate ticket creation, supported by Power Apps to address the lack of two-way communication features, aiming to support Customer Relationship Management (CRM) efforts. The system was developed using Waterfall methodology. The results showed significant improvements in service efficiency: the previous average initial response time of 2 days, 19 hours, and 13 minutes was eliminated due to automatic ticket number assignment; the average issue resolution time decreased from 5 days, 6 hours, and 20 minutes to 42 minutes; and ticket history search time improved from 14 minutes to 2 seconds. The chatbot successfully accelerated the reporting process, enhanced data recording, and reduced the workload of the helpdesk team. This solution significantly improved helpdesk efficiency and strengthened customer engagement.
Mobile Application Development for Waste Management System with K-Means Clustering of Waste Collection Points in Jonggol and Sukamakmur Sub-Districts, Bogor Regency Naufal Aziz; Dadang Iskandar Mulyana; Kastum Kastum
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5250

Abstract

The disparity in the distribution of Temporary Disposal Sites (TPS) within Jonggol and Sukamakmur Districts of Bogor Regency results in inefficiencies in waste collection services, increases travel times, and creates an unequal operational burden on collection fleets. There is no mobile-based digital platform for residents to report TPS conditions in real-time, which further delays responses to waste management. The lack of interactive digital map visualization makes it hard for local sanitation managers to make informed decisions about space. A mobile waste management information system was created using Flutter and Firebase, with the K-Means algorithm used to cluster TPS locations based on their spatial coordinates. The clustering results are presented as an interactive digital map that is integrated with the Google Maps API; this application allows residents to input TPS condition reports, upload visual evidence, and receive notifications about the status in real-time. This project is an extension of our previous web-based work done during the practical internship (KKP) phase but has a larger scope due to a more advanced spatial approach integrated into mobile devices. The system will optimize the distribution efficiency of waste collection services while assisting spatial decision-making processes as well as motivating active participation from residents in maintaining their environment particularly within Jonggol and Sukamakmur Districts under Bogor Regency’s smart city program initiatives.
Image Quality Improvement for Sign Language Gestures Through Gaussian Filter and Contrast Stretching Techniques Dadang Iskandar Mulyana; Muhammad Abdul Aziz Abyan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5254

Abstract

Deaf people use sign language as their primary means of communication. Images of sign language gestures are usually low quality because visual impairments like noise and low contrast prevent an automatic recognition system from working well. This research tries to enhance the quality of images with sign language gestures using two preprocessing methods, namely Gaussian Filter and Contrast Stretching. The first one eliminates noise while keeping important details in the image, and the second increases pixel intensity distribution to make hand gestures more apparent and outlined. An experiment was done on a dataset that includes 54,049 static hand gesture images taken from videos that contain certain sign languages divided into 28 classes for hijaiyah letters. A quantitative evaluation indicated substantial enhancements in processed image quality. The preprocessing method resulted in an average PSNR of 20.13 dB, SSIM equal to 0.8875, and MSE equal to 976.39 for all samples tested confirming that this combination method improves sharpness, structural integrity, and contrast when compared with original unprocessed images significantly. This study recommends using Gaussian Filter along with Contrast Stretching as a practical option for improving the quality of sign language images which can eventually help automated recognition systems that need clear visual input to correctly classify gestures.
A Comparative Analysis of Support Vector Machine and Artificial Neural Network Methods for Predicting Vocational High School Student Graduation Didin Sahrudin; Ferhat Aziz; Choirul Basir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5742

Abstract

Identifying which students may struggle in examinations early on is a critical challenge in vocational schools. This study aims to create and compare two machine learning models to predict the graduation status of Vocational High School (SMK) students majoring in Software and Game Development (PPLG). This prediction is based on their Competency Skills Test (UKK) scores. We used data from 310 students and tested two methods: Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results are very clear: the SVM model performed exceptionally well, achieving an accuracy of 99%. SVM was able to recognize both 'Competent' and 'Not Yet Competent' students in a balanced manner. Conversely, the ANN model's performance was poor, with an accuracy of only 66%. This occurred because the ANN failed to learn and simply guessed that all students would pass. This research concludes that SVM is a highly effective method to be used as an early warning system. With this system, schools can more quickly assist students who are at risk of failing. SVM achieved 99% accuracy with perfect precision for the Competent class and full recall for the Not Yet Competent class. ROC-AUC and PR-AUC indicated excellent separability and strong minority-class detection. ANN achieved only 66% accuracy, predicting all samples as Competent. Learning curves revealed stagnation and failure to learn minority class patterns. Additional baseline models (Logistic Regression, Random Forest) were tested, with SVM outperforming all others consistently. Statistical significance testing using McNemar's test confirmed that SVM provides significantly better classification performance than ANN (p < 0.01).
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions Munawir; Upik Sri Sulistyawati
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2606

Abstract

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.
Classification of Customer Satisfaction with the K-Nearest Neighbor Algorithm in Relation to Employee Performance at PT. Airkon Pratama Ahmad Suprianto; Untung Surapati; Yuma Akbar; Aditya Zakaria Hidayat
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2948

Abstract

PT. Airkon Pratama is the technical consultancy company in the field of maintenance, repair, and operate system. Among its projects are a four-building, multi-story tax office complex. PT. Airkon Pratama experience obstacles to know how its customer satisfaction with their services that is was measured by a questionnaireobtained from work order form. The purpose of this study is to determine how well K-Nearest Neighbor data classification accurately classifies customer satisfaction based on employee performance by PT. Airkon Pratama. The data used in this study is from PT. Airkon Pratama with the data processing using RapidMiner with the K-Nearest Neighbor method which produces an accuracy of 96.53%. Among them four performance indicators were rated as "good", and two as "adequate". Of the 196 that were correctly predicted to be "good," three were "adequate." Most of the 04 respondents gave a positive response indicating their satisfaction with the management of tax office facilities provided by PT. Airkon Pratama in January 2024.
Implementation of an Asset Management System Using the Straight-Line Method of Depreciation Based on Odoo 14 CE at PT Forecastle Indonesia Hendra Ekky Saputra; Rasiban
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2975

Abstract

The purpose of this research is to implement an asset management system based on Odoo 14 Community Edition (CE) using a straight-line method at PT Forecastle Indonesia. Only the straight-line method is chosen as it gives the simple and efficient way to compute the depreciation of the asset over the useful life. Odoo 14 CE is selected for its rich features for asset management for tracking, depreciation calculation, maintenance, and reporting capabilities built in. The study consists of an analysis of the company needs, design based on straight-line method, Odoo 14 CE configuration, and observation and evaluates the implementation results. Key Outcomes: Increased efficiency in managing assets, accurate depredation tracking, reduced manual errors, better inter- department integration. The system is also expected to help prepare reports on assets-financial relations. We will then assess the implementation outputs against improvements in asset management efficiency and effectiveness (e.g. asset condition monitoring, maintenance costs management per asset, asset value tracking). The study will benefit the company by improving its operational and financial performance.
Decision Support System for Internship Acceptance at Digital Connection Using the Simple Additive Weighting Method Bintang Pratama Yuarna Saputra; Sri Sumarlinda; Aprilisa Arum Sari
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2996

Abstract

The internship program serves as a bridge for students into the professional world. At Digital Connection, the current manual selection process for internship candidates leads to inefficiency and potential errors. This study aims to implement a Decision Support System using the Simple Additive Weighting (SAW) method to improve the efficiency of the internship selection process. The SAW method is selected for its capability to provide accurate assessments based on predefined criteria and preference weights, as well as to rank the best alternatives. The system is developed as a web-based application with full access for HR (Admin), including tests as evaluation criteria. This research has resulted in the creation of a decision support system utilizing the Simple Additive Weighting (SAW) calculation method. System testing, conducted using black-box testing, shows that all primary functions and buttons of the system, such as adding, editing, deleting, searching, logging in, managing criteria and sub-criteria data, managing alternative data, calculating scores, exporting, and logging out, function properly and as expected. Furthermore, user testing with 6 criteria and 10 alternative input data points revealed the highest rank of 100% for Wahyu, followed by Noelino in second place with 76%, Hana in third place with 74%, and the lowest rank for Sanjaya with 58%. These results confirm that the calculation system operates effectively according to the researched method and provides clear ranking evaluations to assist HR (Admin) in determining the most suitable internship candidates. The system was implemented on a website using the waterfall model approach as the development method for the research system.
Application of Decision Tree Method for Sales Prediction at PT. Cipta Naga Semesta (Mayora Group) North Jakarta for 2023 Richardviki Beay; Frencis Matheos Sarimole
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2999

Abstract

The purpose of this study is to forecast sales of PT. Cipta Naga Semesta, one of the companies owned by Mayora Group headquartered in North Jakarta using the Decision Tree method during 2023. Decision Tree was chosen because this model identifies key attributes that greatly affect sales in the data and has the ability to predict outcomes by recognizing patterns in historical data. The database used in this analysis includes monthly records of sales, promotions, prices, and other economic characteristics. The findings of the study indicate that the Decision Tree method is very effective in providing accurate sales predictions with a low margin of error. The forecast provides valuable perspectives for company management, which can help them design tighter sales strategies and make better inventory decisions, thereby maximizing operational efficiency and profitability. In addition, the exploration of sales prediction models is one of the future works proposed in this study, which recommends practitioners to explore alternative methods to improve forecast accuracy and robustness.

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