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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,170 Documents
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Design and Performance Evaluation of Small Scale Corn Shelling Machine Chu, Pan Chu
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4480

Abstract

This paper focuses on the design and performance evaluation of small scale corn shelling machine. The motor power of the corn shelling machine is 0.75 hp and rotational speed is 1400 rpm. This motor used v-belt transmission system to drive the shelling cylinder shaft. The number of a belt is used in this transmission system. The standard diameter of the corn shaft is 25 mm. The ASME code equation is used to design the shaft for corn shelling machine. Single-row deep groove ball bearing is selected for left and right bearing of corn shaft. The average shelling efficiency, output capacity and damage efficiency of corn were compared with difference spike teeth and various speeds. The average shelling efficiency of 24, 27 and 30 spike teeth are 87 %, 94 % and 81 %. The average output capacity of 24, 27 and 30 spike teeth are 232 kg/h, 281 kg/h and 263 kg/h. The average damage efficiency of 24, 27 and 30 spike teeth are 13 %, 6 % and 19 % respectively. The maximum shelling efficiency and output capacity are found 27 spike teeth at cylinder speed of 690 rpm. The minimum damage efficiency is 6% at 27 spike teeth.
Evaluasi Kematangan Manajemen Proyek Perangkat Lunak: Studi Kasus Pengajuan Tunggal Pengembangan Kepabeanan Karantina di Badan National Single Window Indonesia Jeffry Adityapriatama; Dana Indra Sensuse
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4483

Abstract

This investigation provides a critical assessment of the governance practices in project management within the development of the Single Submission Quarantine Customs (SSM QC) system at the Indonesia National Single Window Agency (LNSW). Employing the Kerzner Project Management Maturity Model (KPM3), the study meticulously compares existing management operations against the established Project Management Body of Knowledge (PMBOK) standards. It uncovers pivotal governance deficiencies that hinder the maturation of project management processes. The research sheds light on the necessity for systematic improvements, particularly in areas of risk assessment, human resources, procurement, and quality control. Recommendations from this study advocate for the adoption of robust governance frameworks and strategic management enhancements. The implementation of these recommendations is projected to significantly uplift LNSW's strategic project alignment, operational efficiency, and overall effectiveness, marking a substantial advancement in the domain of software project management and setting a precedent for future research in this area.
Building an Automated Guided Vehicle Based on UWB Technology Haryono; Santoso, Handri
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4487

Abstract

The development of automated guided vehicles (AGVs) for indoor environments necessitates precise positioning technology to enable accurate navigation within confined spaces. Ultra-Wideband (UWB) technology has proven to be a leading solution for this purpose, known for its high accuracy, low latency, and resilience to interference. This study presents a specialized approach to AGV localization within a room, utilizing UWB technology to achieve reliable movement and positioning. We conducted a comparative analysis of two UWB modules, DWM1000 and DWM1001, evaluating their performance and suitability for AGV applications. Although both modules provide high accuracy, the DWM1001 was chosen due to its integrated microcontroller, simplified setup, and enhanced compatibility with indoor navigation. The DWM1001’s efficient integration and power management make it ideal for environments requiring precise and dependable AGV operation. This paper details the methodology for selecting the DWM1001 and demonstrates how it enables robust AGV navigation with minimal drift, achieving a positioning accuracy of approximately 10 cm—an acceptable margin for indoor applications. Through rigorous testing and evaluation, we observed consistent performance, validating the DWM1001 as an effective solution for small-scale AGV systems. This approach not only provides a reliable foundation for deploying UWB technology in compact indoor settings but also addresses a gap in current research on high-precision, small-scale AGV localization.
Using the Machine Learning Algorithms for Accurate Prediction of Diabetes Emmanuel Imuede Oyasor; Gbadebo, Adedeji Daniel
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4488

Abstract

Diabetics has proven to be the most threatening illness affecting the body system. It is associated with many consequences, including blindness, kidney failure, amputations, heart failure, microvascular and macrovascular complications, which affects millions of people across the world and has contributed to increased mortality. Studies shows that effective management and early detection of diabetes remains crucial for preventing its complications and improving the patient. According to available data, we use machine learning algorithms, including the Support Vector Machine (SVM), AdaBoost (ADA), Neural Networks (NNET), K-Nearest Neighbors (KNN), Random Forest (RF), and Logit Boost (LOGIT), for the accurate prediction of diabetes amongst patients. We find that the Logit Boost and AdaBoost stand out as the top performers for predicting diabetic patients, with balanced and reliable performance across various evaluation metrics. They exhibit high accuracy, strong AUC scores, and good overall performance across multiple metrics, making them suitable for this classification task. Neural Networks show excellent precision and low log loss, indicating strong probabilistic predictions, but their lower specificity suggests a higher false-positive rate. Random Forest demonstrates good recall but lower accuracy on the test set, indicating potential overfitting to the training data. SVM and KNN perform the weakest across most metrics, suggesting they may not be the best choices for this prediction task.
Design and Development of a Hybrid Tricopter Fixed-Wing UAV for Precision Agriculture Febrianto, Rokhmat; Yeoh, Jessie Charydon; Putra, I Gede Arinata Kusuma; Sasmito, Ayomi; Alfiansyah, Agung
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4489

Abstract

Precision Agriculture (PA) relies on innovative technologies to enhance efficiency and sustainability in agricultural practices. This study focuses on the design, simulation, and evaluation of a Hybrid Tricopter VTOL UAV tailored for PA applications. The UAV combines hover and fixed-wing flight modes, enabling versatility in data collection and farmland monitoring. Through rigorous simulations, the hover mission demonstrated the effectiveness of PID controllers in stabilizing roll, pitch, and yaw dynamics, achieving high positional accuracy with minimal error rates. The transition mission validated the UAV’s adaptability, showcasing smooth transitions between flight modes under varying tilt rates. Additionally, electronic component simulations confirmed the propulsion system operates efficiently within thermal and electrical limits, ensuring durability and energy efficiency. The findings highlight the UAV’s reliability, adaptability, and operational readiness, laying a foundation for advanced UAV applications in PA and beyond. This work underscores the potential of UAVs in optimizing agricultural productivity and sustainability.
Enhancing Vocational Education Through Digital Marketing Strategies: A Prototype Approach Yullindo; Nandra; Idris, Iswandi
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4491

Abstract

This study investigates the challenges faced in digital marketing transformation within vocational education at Politeknik LP3I PSDKU Campus Padang. The problem stems from traditional marketing methods being less effective in attracting prospective students in a digital era. Employing the Software Development Life Cycle (SDLC) prototype methodology, the research aims to develop a responsive marketing information system tailored to user needs. Objectives include identifying key marketing tools and enhancing user engagement through interactive platforms. Results demonstrate improved enrollment figures and user satisfaction, validating the effectiveness of digital marketing strategies. The findings contribute to a framework that vocational institutions can utilize to modernize their marketing approaches effectively
Implementasi Face Recognition Pada Aplikasi Absensi Berbasis Android Menggunakan Algoritma Haversine Siddiq Assegaf, Djafar; Azhar, Ryfial; Pusadan, Yazdi; Anggun Pratama, Septiano; AR. Lamasitudju, Charunnisa
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4494

Abstract

Android-Based Attendance Application, Face Recognation, Haversine Algorithm, Management System. The attendance system is a method for managing employee presence, which contributes to productivity and accountability. This study aims to implement an Android-based attendance application that utilizes face recognition technology and the Haversine algorithm to enhance the accuracy and efficiency of the attendance process. Face recognition is applied to automatically verify user identity and reduce the risk of fraud in the attendance process. The system integrates the Haversine algorithm and face recognition, where the Haversine algorithm is used to calculate the distance between the employee's location and the office, ensuring that attendance can only be recorded within a predetermined radius. The results indicate that this system is effective in determining employee attendance status with high accuracy, recording employees within a radius of ≤ 30 meters as present. Additionally, the use of face recognition technology accelerates the attendance process and improves accountability. These findings open opportunities for further research in integrating technology into human resource management and are expected to enhance transparency and efficiency in managing employee attendance across various sectors.
Penentuan Tingkat Stres berdasarkan Bio-Parameter Menggunakan Variasi Kernel Support Vector Machine Daffa Syah Alam; Rokhana, Rika; Arief, Zainal
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4495

Abstract

System for detecting a person's stress level based on bio-parameters is blood pressure, heart rate, and respiratory rate. Measurements of blood pressure, heart rate, and respiratory rate in order to detect the condition of a person's stress level are carried out non-invasively or don’t damage the nervous tissue in the body and routinely. Heart rate measurement using MAX30102 sensor on the finger. Measurement of blood pressure using the MPX2050GP pressure sensor by placing cuff on the person's arm. While measuring the breathing rate using the MAX9814 micondensor sensor. In determining or classifying stress level conditions from non-invasive measurement parameters of blood pressure, heart rate and respiratory rate using Support Vector Machine (SVM) method with specified kernel variations. The classification of stress level conditions consists of four classes including normal, mild stress, moderate stress and severe stress. So that a dataset of 71 data is obtained with the data augmentation process and the accuracy of each SVM kernel variation used is obtained.
Metode Multi-Criteria Decision Making (MCDM) untuk Rancang Bangun Sistem Informasi Pengambilan Keputusan Rekrutmen Dosen Unggul berbasis Web Hadi, Abrar; M. Syahputra; Khair, Rizaldy
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4496

Abstract

Excellent Lecturers are human resources (HR) that are very necessary in the world of education so that they can educate students to become superior human resources. Apart from that, all universities in Indonesia play an active role in improving the quality of education. One important factor that determines the quality of education in higher education is the quality of the lecturers who teach. Excellent lecturers are lecturers who are sought after and needed by all universities. However, in reality, the lecturer recruitment process in educational institutions often experiences obstacles, such as inaccurate decisions, errors in decision making, and late decision making. This can affect the quality of education provided and the effectiveness of the educational institution itself. Therefore, selecting superior and qualified lecturers is very important. So, we need a decision support system to help the recruitment process for superior lecturers more effectively and efficiently. Manual decision making in the recruitment process is considered less objective and can result in decisions that are not in accordance with wishes. The Multi-Criteria Decision Making (MCDM) method is a method that can be used to assist decision making in deciding which superior lecturers to recruit. The urgency of this research is to help make decisions effectively and efficiently. The aim of this research is to build a decision support system for the lecturer recruitment process using the web-based Multi-Criteria Decision Making (MCDM) method.
Implementasi Naïve Bayes Classifier untuk Sentimen Produk Kecantikan Berdasarkan Ulasan Female Daily Pramesti Melinea Berlianti; Erwin Yudi Hidayat
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4499

Abstract

Industri kecantikan di Indonesia mengalami pertumbuhan yang pesat, terlihat dari bertambahnya perusahaan kecantikan setiap tahun. Salah satu produk kecantikan yang banyak diminati adalah moisturizer, yang menghasilkan banyak ulasan di platform kecantikan, seperti Female Daily. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna terhadap produk kecantikan untuk memahami preferensi konsumen, menggunakan metode Naïve Bayes Classifier. Sebanyak 1.050 ulasan diolah melalui proses preprocessing hingga menghasilkan 1.042 data bersih, terdiri dari sentimen positif (84.36%), negatif (12.96%), dan netral (2.69%). Untuk menangani ketidakseimbangan data diterapkan teknik SMOTE, dan pencarian hyperparameter optimal dilakukan dengan GridSearchCV, yang meningkatkan akurasi model dari 79% menjadi 82,77%. Hasil ini menunjukkan bahwa penerapan GridSearchCV berperan penting dalam meningkatkan akurasi dan stabilitas klasifikasi sentimen pada produk kecantikan.

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