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Muhammad Wali
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ijsecs@lembagakita.org
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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 41 Documents
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Search results for , issue "Vol. 4 No. 3 (2024): DECEMBER 2024" : 41 Documents clear
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions Munawir; Sulistyawati, Upik Sri
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 Suprianto, Ahmad; Surapati, Untung; Akbar, Yuma; Hidayat, Aditya Zakaria
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 Saputra, Hendra Ekky; 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 Saputra, Bintang Pratama Yuarna; Sumarlinda, Sri; Sari, Aprilisa Arum
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 Beay, Richardviki; Sarimole, Frencis Matheos
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.
Automatic Detection of Skin Diseases Using Convolutional Neural Network Algorithms Tundo; Prayogo, Fadillah Abi; Sugiyono
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.v4i3.3021

Abstract

Skin diseases are a major health concern in Indone sia and they can seriously impact a patient’s quality of life. The problem is aggravated by humid tropical climate, limited access to healthcare facilities, and a lack of trained dermatology personnel. The cases in Indonesia are many, and the diagnosis and treatment of skin diseases are delayed, which makes the patient's condition worse. Based on data from the Ministry of Health (Kemenkes), the prevalence of skin disease in Indonesia is 0.62 cases per 10,000 population with the highest prevalence in Eastern Indonesia. Developing a Skin Disease Detection System Based on Convolutional Neural Network (CNN) algorithms. However, CNN algorithms are widely used in image recognition and classification, and can act as an automatic diagnostic system. This system has been developed to aid in diagnosis and improve patient access to dermatological care, especially for remote communities. Users can reach out for services at any time and any location, a practical solution for treating skin health problems. This study's results are anticipated to lower the diagnostic delays and improve the treatment outcomes while offering quick access to reliable dermatological service. This is a great effort on global level for any skin disease supporting to improve life of human lives from skin health issues.
Analysis of Scooter Spare Parts Sales at Harapan Indah Scooter Using the K-Means Algorithm Sarimole, Frencis Matheos; Lingga, Tracy Olivera
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.v4i3.3026

Abstract

: K-means clustering algorithm has been used in this study to analyze the sales performance of scooter spare parts at Harapan Indah Scooter. By using the K-means method, researchers can classify products into 3 categories according to their sales volume. The purpose of this analysis is to identify patterns in sales data and compare the characteristics of each product group. Researchers can see the output from the previous step shows three clusters: Low, Medium, and High Sales. Associating products with these categories Empowers improved tracking of sales movements and fluctuation trends in product options. The findings of this study can be useful in the field of inventory management and to develop marketing strategies to increase product sales. Companies can find out which products fall into which categories and therefore can make better decisions on how to manage stock and promotional efforts. These findings are the first step to maintain and improve sales performance and optimize Harapan Indah Scooter business
Vehicle License Plate Object Detection for Vehicle Registration Using Fuzzy Logic Alannuari, Fiky; Sarimole, Frencis Matheos; Mulyana, Dadang Iskandar
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.v4i3.3055

Abstract

Object detection of vehicle license plates plays a role in the efficiency of vehicle data collection systems. There are many factors that make the accuracy and speed of detection on vehicle license plates less than optimal, causing errors in the detection process. The factors that affect the accuracy of object detection of vehicle license plates include clarity, lighting, shadows, color, font type, weather, and others. Based on the advantages of the Fuzzy Logic approach in handling various vague factors and uncertain data, it is hoped that this method can help the detection process to be more accurate and faster. This research aims to develop a method for detecting vehicle license plate objects using the Fuzzy Logic approach so that it can be applied in diverse environments to produce data with consistent accuracy. This research involves the development of software integrated with computers and cameras for vehicle license plate recognition, and also takes some data sources and code from libraries already available in the programming language used. The results of the tests conducted, detection using this Fuzzy Logic approach has an accuracy rate of up to 93.33% and the accuracy of reading the text stored in the database reaches 63.66%.
2D Platformer Game Prototype on Indonesian History Using Scratch Akbar, Yuma; Al Ammaar, Mohammad Farroos
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.v4i3.3070

Abstract

In this study, we would like to share a prototype of a 2D platformer game in Scratch centered on the development of Indonesian history that can increase students' interest and motivation in learning history. In search of a more entertaining and successful alternative to the bad lecture situation, the demand for interactive learning media. Visual programming is chosen with Scratch because it is easier to create educational games in this language. Its creation involves the implementation of visual components such as characters, background environments, UI, etc. Data Evaluation provides a positive level of acceptance to students with an average student evaluation score of 4.1/5 Positive responses were obtained for game elements, story content, and ease of operation First, the results of the evaluation of the Validation of 2D Platformer Games in Indonesian History as a Learning Tool. Through student activeness, a more active way of responding to historical material is being implemented by use. Game development has the ability to become a new educational media if it is well structured and organized.
Sentiment Analysis of the Tapera Law on Platform X Using Naive Bayes Algorithm Bimantoro, Dava Sevtiandra; 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.v4i3.3077

Abstract

The implementation of the 2016 Public Housing Savings Law (UU Tapera) aims to help legal and informal workers have decent houses through the management of housing savings funds by BP Tapera. However, when implemented, this program experienced obstacles amidst various problems including the transparency of the fund collection and management system, the unevenness of benefit provision, and variations in public perception. Sentiment analysis was conducted on Twitter data for sentiment regarding the Tapera Law to obtain public perception with Naïve Bayes. This approach classifies sentiment into positive, negative, and neutral. The accuracy of the Analysis Results was 62.47% (343 negative sentiments, 23 neutral, and finally 32 positive sentiments). The public mostly has negative sentiment towards the Tapera Law, because many of them are afraid of losing justice and effectiveness with this policy. These results underline the need to intensify transparency and communication of the benefits of the Tapera Law and its mechanisms to increase public acceptance and trust.

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