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INDONESIA
Computing and Information System Journal
Published by PT. Indo Compt Publisher
ISSN : -     EISSN : 31093248     DOI : -
Core Subject : Science,
Computer Science & IT
Computing and Information System Journal is a peer-reviewed international journal that publishes high-quality and original research contributions in the fields of computing and information systems. The journal aims to bridge the gap between theoretical advances and practical applications in computer science and information technology. The journal welcomes empirical studies, theoretical papers, case studies, and state-of-the-art reviews. Contributions from academics, researchers, and industry practitioners are equally encouraged. 1. Computer Science Artificial Intelligence Machine Learning Computer Vision Software Engineering Algorithms & Computation Cybersecurity Distributed & Parallel Computing 2. Information Systems Management Information Systems (MIS) Enterprise Systems Business Intelligence Decision Support Systems IT Governance Health Informatics E-Government Systems 3. Data and Network Technologies Big Data Analytics Data Mining Cloud Computing Edge/Fog Computing Internet of Things (IoT) Sensor Networks Computer Networks 4. Human-Centered Computing Human-Computer Interaction (HCI) User Experience (UX) Educational Technology E-Learning Systems Assistive Technologies
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 19 Documents
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THE EFFECT OF IMPERFECTIVE DATA SAMPLING METHOD ON SUPPORT VECTOR MACHINE ACCURACY Erna Cholida, Ferdy Maulana; Arifianto, Deni; Umilasari, Reni
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

Sentiment analysis is used to understand the direction of public opinion, but problems arise due to the unbalanced distribution of sentiment data, where one class dominates. This imbalance causes classification models such as Support Vector Machine (SVM) to be biased towards the majority class, which results in decreased accuracy and generalizability of the model. This study aims to assess the effectiveness of two data balancing techniques, namely, SVM-SMOTE, and ADASYN, in improving SVM performance. The research data was taken from social media platform X (Twitter), and testing was conducted using the K-Fold Cross Validation method (K=2, 5, and 10) using evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that without data balancing, the SVM model can only achieve an average accuracy of 76.34% and F1-score of 62.38%, which reflects the weakness in recognizing minority classes. The application of the two balancing methods successfully improved the model performance. ADASYN increased the F1-score to 67.94%, while SVM-SMOTE showed the most optimal results with 82.4% accuracy and 74.02% F1-score. These findings indicate that SVM-SMOTE is the most effective technique in handling data imbalance and improving sentiment classification accuracy equally.
UI/UX design in food health start up using design thinking method and gestalt principles MA, M LUTFI
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

Healthy food is an absolute necessity and a primary need for human life. However, many foods are currently served in unhealthy ways, such as using raw materials, processing methods, and long storage times. A food retailer and food outlet must be able to meet the need for healthy food, both in terms of raw materials, processing methods, and storage times until it is served to consumers. The FoodHealth startup will provide a service for daily healthy food subscriptions for consumers. The research method used is the design thinking method, the analysis uses SWOT, the feasibility study analysis uses TELOS while User Interface has been created using the Gestalt principle. The UI design testing process uses 10 Usability Heuristic indicators with a test result of 83%. This proves that the designed digital start-up FoodHealth has answered user needs. This FoodHealth start-up is expected to provide convenience for users in finding information on healthy food programs or healthy food subscriptions, including ease of delivery.
Implementation of E-Government to Increase Transparency and Efficiency at the Communication and Information Service (A Case Study in Semarang City) Tri Widayati, Yohana; Wahyu Binabar, Sattriedi; Widjaja, Stephanus; Saguruk, Thepanus
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

The advancement of information technology has encouraged local governments to adopt e-Government systems to improve the quality of public services. This study aims to analyze the implementation of e-Government at the Department of Communication and Informatics (Diskominfo) of Semarang City through the Sapa Mbak Ita application as a public complaint platform. The research was conducted using observation, interviews with the Head of the Department, literature review, and a system development approach based on the waterfall model. The results show that the citizens of Semarang actively use various digital complaint channels, with the highest number of reports submitted via WhatsApp and the Sapa Mbak Ita mobile application. Out of 6,129 reports received in 2023, the majority were followed up. However, several issues were identified, including delayed complaint resolution and user concerns regarding ease of use and the security of personal data. This study concludes that Sapa Mbak Ita has contributed positively to enhancing public service transparency, although improvements are still needed in terms of efficiency and user data protection.
CLASSIFICATION OF THE SOCIOECONOMIC STATUS OF PROSPECTIVE GROOMS USING THE MODIFIED K-NEAREST NEIGHBOR (MKNN) ALGORITHM Sari, Rosi Ernita Sari; Ali Muharom, Lutfi; Abdurrahman, Ginanjar
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

Marriage is an important moment in life that is influenced not only by emotional aspects, but also by socioeconomic factors. The socioeconomic status of the prospective groom can affect the harmony of the household that will be built. This study aims to classify the socioeconomic status of prospective grooms using the Modified K-Nearest Neighbor (MKNN) algorithm and evaluate its performance through accuracy, precision, and recall measurements. The dataset used consists of 200 data points on prospective grooms obtained from the BKKBN (National Family Planning Agency) of Bondowoso District, with attributes including occupation, source of income, and income value. The classification process involves data pre-processing, Euclidean distance calculation, validation of training data, weighted voting, and K-fold Cross Validation. The test results showed that MKNN was able to provide good classification performance, with the highest accuracy of 88%, precision of 91.60%, and recall of 88% in a specific K-Fold scenario. This study shows that the MKNN algorithm is effective in classifying the socioeconomic status of prospective grooms and can be used as a reference for further research.
OPTIMIZATION OF LAYING DUCKS FEED COMPOSITION USING THE K-MEANS CLUSTERING ALGORITHM METHOD IN SECANG SUBDISTRICT Arifah, Nur; Primadewi, Ardhin; Yudatama, Uky
Computing and Information System Journal Vol. 1 No. 3 (2025): Data Science, UI/UX, and E-Government for Decision Making
Publisher : IndoCompt Publisher

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Abstract

Indonesia has significant potential in duck farming, particularly as a source of eggs and meat. However, the productivity of local laying ducks remains low due to the traditional feed management practices still widely used. In Secang District, Magelang Regency, farmers often determine feed composition based on availability and peer recommendations without proper consideration of nutritional requirements. This leads to imbalanced nutrition, negatively affecting egg production. This study aims to provide optimal feed composition recommendations using the K-Means Clustering algorithm. The algorithm clusters feed data based on nutritional content and egg production performance. Through this approach, farmers are expected to gain more accurate and efficient information in determining feed composition, thereby improving productivity, reducing operational costs, and enhancing product quality. Furthermore, this research contributes to the development of knowledge in both information technology and animal husbandry by applying machine learning techniques in the agricultural sector.
SENTIMENT ANALYSIS OF COMMENTS ON THE TRAGEDY OF JULIANA MARINS' FALL ON MOUNT RINJANI ON APPLICATION X USING THE MULTINOMIAL NAÏVE BAYES METHOD ika putri maulida; Wiwik Suharso; Ginanjar Abdurramanc
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

This study aims to analyze the sentiment of user comments on the X (Twitter) platform regarding the tragic fall of Brazilian hiker Juliana Marins on Mount Rinjani. A total of 1006 comments were collected through a crawling process from June 21, 2025, to July 11, 2025. The research stages include data labeling, text preprocessing (cleaning, case folding, tokenizing, stopword removal, and stemming), N-Gram formation, and feature weighting using TF-IDF. The Multinomial Naïve Bayes algorithm was employed for sentiment classification into three categories: positive, negative, and neutral. Data imbalance was addressed using the Random Oversampling (ROS) technique. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-Score metrics. The results show that the model achieved an accuracy of 85%, with precision, recall, and F1-Score values indicating effective sentiment classification. Neutral sentiment was found to be the most dominant category among user comments. These findings offer a comprehensive overview of public perception regarding the incident and can serve as a useful reference for decision-making and communication strategies related to similar issues
EARLY DETECTION OF DIABETES MELLITUS TYPE 1, TYPE 2, AND GESTATIONAL DIABETES MELLITUS (GDM) USING WEBSITE-BASED CERTAINTY FACTOR Rayhan Ibrahim; Guruh Wijaya; Dudi Irawan
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

The development of information technology plays an important role in supporting healthcare services, particularly in assisting the early detection of diseases. One chronic disease that requires early detection is Diabetes Mellitus, which consists of Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM). These three types of diabetes have similar early symptoms, making them difficult to recognize by the general public. Therefore, this study aims to design and develop a web based expert system to detect Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM) using the Certainty Factor method. This method is used to calculate the level of diagnostic confidence based on symptoms selected by users, referring to confidence weights provided by medical experts. The problem-solving process is carried out through Certainty Factor calculations using a weighted average approach to generate the percentage of diagnostic confidence. Based on testing and validation results through manual calculations and expert evaluation, the developed system achieved an accuracy rate of 87.10%. The results indicate that this expert system can be used as a supporting tool for the early detection of Diabetes Mellitus prior to further medical examination.
Clustering of Lecturer Performance Based on Internal Data and Questionnaire Using the X-Means Algorithm Ali Afendi; Rosita Yanuarti; Qurrota A’yun
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

Lecturer performance evaluation is essential for improving the quality of higher education. In the Informatics Engineering Study Program at Universitas Muhammadiyah Jember, assessments are still conducted manually, leading to inefficiency and subjectivity. This study aims to cluster lecturer performance based on internal data—credit load, publications, attendance, and students’ average grades—and student perceptions collected through questionnaires, using the X-Means Clustering algorithm. X-Means was chosen because it can automatically determine the optimal number of clusters using the Bayesian Information Criterion (BIC) and log-likelihood. The research involved data collection, cleaning, normalization with the Min-Max method, implementation of X-Means, and visualization using Principal Component Analysis (PCA). The results identified five distinct clusters: high-performing lecturers, lecturers with high publication output, research-active lecturers with low student grades, low-performing lecturers, and stable-performing lecturers. These findings provide an objective view of lecturer performance variations and can serve as a basis for developing effective strategies for lecturer improvement and continuous performance evaluation
IoT-Based Sleep Quality Monitoring Simulation With Sleep Condition Analysis Using Fuzzy Logic M LUTFI MA
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

This research aims to develop a low-cost IoT-based sleep quality Monitoring Simulation with Sleep Condition analysis using fuzzy logic for real-time physiological data acquisition and classification. The study employed the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The system integrates MAX30100 heart rate sensor, DHT22 temperature sensor, and MPU6050 motion sensor with an ESP32 microcontroller. A Mamdani fuzzy inference system was implemented to classify Sleep Conditions based on heart rate, body temperature, and body movement parameters. The system was simulated using Wokwi and Cisco Packet Tracer, with fuzzy rules developed in MATLAB and embedded programming in Arduino IDE. The Blynk IoT platform was utilized for remote monitoring. Validation included Black Box Testing and User Acceptance Testing (UAT) with ten respondents. The system successfully acquired real-time physiological data and transmitted it to the Blynk cloud platform. The fuzzy logic algorithm effectively processed sensor data uncertainty, classifying health conditions into "Healthy," "Warning," or "Critical" status. Black Box Testing confirmed all main functions operated according to specifications. UAT yielded an 88% satisfaction score, indicating the system is highly feasible, user-friendly, and beneficial for independent sleep monitoring. This research contributes a practical, affordable IoT-based sleep monitoring architecture integrating fuzzy logic for Sleep Condition classification on embedded systems, offering an accessible alternative to conventional clinical methods for personal health management.

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