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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 804 Documents
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Analisis Sentimen Masyarakat Terhadap Rencana Kenaikan PPN 12% Di Indonesia Pada Media Sosial X Menggunakan Metode Decision Tree Hardyatman, Intan Diah; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6573

Abstract

This study analyzes public sentiment towards the planned increase of Value Added Tax (VAT) to 12% in Indonesia using data from X social media. The VAT hike could trigger an increase in overseas spending and higher prices for products and services in Indonesia, potentially reducing sales and weakening industries. This proposal also received widespread attention on social media X. The VAT increase plan has pros and cons, triggering many discussions on social media. The Decision Tree classification method was used to process the data obtained through crawling and text preprocessing. This research compares 80% training data and 20% test data consisting of 1000 data, with details of 285 negative sentiments and 715 positive sentiments in the dataset. In this case, it can be described that X social media users towards the plan to increase VAT by 12% in Indonesia tend to be positive. This research aims to analyze people's sentiment towards the plan to increase VAT by 12% in Indonesia using Decision Tree and identify factors that influence the sentiment. The results of the analysis show that Decision Tree succeeded in increasing the accuracy by 81.34% of sentiment classification compared to previous methods, such as Naïve Bayes with an accuracy rate of 63.1%. The results of this study are expected to help the government in a more responsive fiscal policy.
Analisis Kepuasan Masyarakat terhadap Pelayanan Publik menggunakan K-Means Clustering Hariyanto, Yenik; Primadewi, Ardhin; Hanafi, Mukhtar
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6577

Abstract

This study aims to analyze data clustering using the K-Means Clustering method in order to understand certain patterns contained in public satisfaction data on public services. The problem of this study focuses on how to optimally group data to evaluate the quality of service indicators based on 9 indicators in the Public Satisfaction Survey (SKM). The purpose of this study is to divide data into several clusters so that it can provide a clear picture of the differences in quality between service groups. The method used in this study is the K-Means Clustering method, which consists of several stages, namely determining the number of clusters, determining the initial center point, calculating the distance of data to the center point, grouping data, updating the center point, and providing cluster labels. Evaluation of the quality of clustering results is carried out using two evaluation metrics, namely the Silhouette Score and the Davies-Bouldin Index. The results showed that the data was divided into two clusters with a Silhouette Score value of 0.515 which indicated a fairly good clustering quality. In addition, the Davies-Bouldin Index value of 0.784 indicates that the clusters formed have a fairly good distance between each other. The results of this analysis provide an overview that the first cluster has a higher quality of service compared to the second cluster based on the average value of the service indicators measured. This study is useful in providing more structured and accurate information regarding service quality, so that it can be a basis for policy makers to improve service performance in the future. In addition, this study can also be a reference for further research in the application of the K-Means method for similar cases with a focus on evaluation and development of public services.
Penerapan Metode Fuzzy Tsukamoto Dalam Sistem Pakar Diagnosis Penyakit Pada Sapi Baraputri, Jennie Nadia; Sanjaya, Fadil Indra
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6568

Abstract

Cattle are one of the most widely cultivated animals in Indonesia. Cattle diseases are also a major challenge in animal husbandry and can affect productivity and animal welfare. To face these challenges, an accurate disease detection and diagnosis system is needed. Such a system is essential to reduce the risk of disease spread and speed up the treatment process. Research was conducted to develop an expert system using the Fuzzy Tsukamoto method. This method was chosen because it can handle data uncertainty in clinical symptoms. To determine the diagnosis results, the system consists of five main stages, namely data collection of disease symptoms and characteristics, data fuzzification, rule base formation, fuzzy inference process, and defuzzification. The system is also designed by including symptom and characteristic variables, as well as diagnostic rules that help the diagnosis process automatically. Based on the fuzzy inference and defuzzification process that has been carried out, the final result for the diagnosis of Herpes disease is 90% and Mastitis disease is 90% which means the severity of the disease is “Severe”.
Sistem Pendukung Keputusan Pemilihan Calon Penerima Beasiswa dengan Multi Objective Optimization on The Basis of Ratio Analysis Kuncorowati, Dewi; Purwanto, Eko; Permatasari, Hanifah
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6579

Abstract

The selection of scholarship candidates has so far been conducted manually and is not well-documented. The selection process is based on the opinions or personal preferences of the selection team, which can lead to unfairness. There is no consistent standard for evaluating the criteria of scholarship candidates.This study develops a Decision Support System (DSS) for selecting school scholarship candidates using the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method. This method is chosen for its ability to handle various qualitative and quantitative evaluation criteria, such as academic achievement, economic conditions, and extracurricular participation. The system is designed to produce candidate rankings objectively and transparently, facilitating fair and accurate decision-making by the school. Testing results indicate that the MOORA-based DSS can provide accurate and consistent recommendations, enhancing the efficiency of the selection process and stakeholder satisfaction. This research also opens opportunities for further development by integrating technologies such as machine learning to enhance system capabilities. The results of this study can assist in determining acceptance of the scholarship
Optimizing Consistency and Efficiency of Simakip’s Frontend Architecture Through Implementation of Atomic Design Method Nova, Sabrina Qodri; Dzikrillah, Akhmad Rizal
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6607

Abstract

The Research and Community Service Performance Management System (SIMAKIP) is a centralized website that facilitates UHAMKA academicians to report research and community service activities periodically, by submitting valid research and scientific publications to the ristekdikti website for performance assessment. The problem is that centralized information systems have a wide range of feature complexity, making it important to ensure design consistency and efficiency of interface component development. To address the problem, the implementation of an atomic design approach is used as an innovative method that utilizes reusable modular components in five stages namely atom, molecule, organism, template and page for structured and integrated component development across the application. The purpose of this research is to improve the frontend architecture of SIMAKIP UHAMKA to optimize the performance of the development team and SIMAKIP as a whole, which will contribute to the advancement of research and community service within the academic community. Based on User Acceptance Test (UAT) testing by conducting 10 test cases to 10 participants, it was found that the overall SIMAKIP frontend architecture achieved a percentage of conformity of 95.96% in the Very Good category. These results prove the success of the atomic design approach to the consistency and efficiency of UHAMKA SIMAKIP and have the potential to become a model for other institutions as well as a reference for research in the field of other academic information systems.
Penerapan Aplikasi Penerimaan Siswa Baru di Sekolah Menengah Kejuruan Berbasis Android Menerapkan Model Waterfall Sugiyarti, Triyana; Adrian, Qadhli Jafar
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6620

Abstract

New Student Admission (PSB) is an important activity in the education system that requires an effective and efficient process. YP 17 Baradatu High School, located in Way Kanan Regency, still uses manual methods in the PSB process, which causes many obstacles such as data input errors, delays and difficulties in managing information. Therefore, this research aims to develop an Android-based New Student Admissions application that can simplify the PSB process in the school, by applying the Waterfall software development model. The Waterfall model was chosen as a development method because of its systematic and sequential nature, which is suitable for projects that have clear and fixed needs. This application is expected to be able to automate the entire PSB process, starting from registration, data verification, announcement of selection results, to managing new student data in an integrated and efficient manner. In this research, the solution provided is an Android-based mobile application that allows prospective students and schools to access and manage PSB data more practically. The main objective of this research is to design and implement an Android-based PSB application that can increase the effectiveness and efficiency of the new student admission process at SMA YP 17 Baradatu. With this application, it is hoped that it can reduce errors in data management, speed up the admission process, and make it easier for prospective students to register online. The contribution of this research is the development of an Android-based PSB application that can be adapted by other schools that face similar problems. The interim results achieved show that this application has succeeded in simplifying the registration process and facilitating communication between prospective students and the school. Initial testing of the application also showed a positive response from users regarding the ease of use and functionality of the application in supporting the new student admissions process.
Klasifikasi Ras Kelinci Menggunakan Convolutional Neural Network (CNN) untuk Optimasi Sistem Identifikasi Visual Huda, Maasyaril Kirom Mi’Rojul; Witanti, Arita
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6627

Abstract

Rabbits are mammals that come in many varieties with unique and diverse physical characteristics. Differentiating various types of rabbits, especially those with physical similarities and color patterns, is a challenge for some people because of their similar visual appearance. The purpose of this research is to develop a Convolutional Neural Network (CNN)-based rabbit breed classification system using MobileNetV3 architecture. A dataset of 1,500 images of three rabbit breeds (bligon, hyla, and new zealand white) was processed through resizing, augmentation, and normalization to improve data quality. The model was trained using Adam's optimizer with 97% accuracy on the validation data and 90% on the external dataset, showing good generalization ability. These results confirm the effectiveness of CNNs over manual methods in visual pattern recognition, while overcoming time constraints and human error. However, limitations in dataset variations, such as lighting and image capture angle, affect the generalization of the model. This research not only supports the efficiency of livestock management but also shows the great potential of AI application in Indonesia's livestock sector. Development of more diverse datasets and exploration of other model architectures are recommended for future performance improvements.
Penerapan Metode Design Thinking untuk Perancangan UI/UX: APIW Aplikasi Image Watermarking Aziz, Faruq; Yanto, Yanto; Saputri, Daniati Uki Eka
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6204

Abstract

This study aims to apply the Design Thinking methodology in the design of the user interface (UI) and user experience (UX) for APIW, an image watermarking application based on invisible watermarking for copyright protection. The Design Thinking methodology is applied through five stages: Empathize, Define, Ideate, Prototype, and Test. The innovation in this research lies in the design of the UI/UX for APIW, focusing on the invisible watermarking feature that can still be verified even when the image is resized or reformatted. This study creates an intuitive interface for users without technical skills. Design evaluation using tree testing shows a success rate of 94.71% in finding the desired features, while the search system achieves 98%. Thus, this application is designed to provide ease for users in adding watermarks to their images without compromising aesthetics. This study concludes that the application of Design Thinking in the UI/UX design of APIW successfully creates a solution that is effective, intuitive, and responsive to user needs.
Sistem Pendukung Keputusan Pemilihan Kepala Karyawan Produksi Menggunakan Metode Simple Additive Weighting (SAW) Putra, Abednego Erindra; Yasin, Ikbal
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6229

Abstract

CV. Wahyu Putra is one of the companies operating in the agricultural sector that has not utilized information technology in developing its company. This includes the problem of selecting head employees which is still determined subjectively. This problem can be resolved by conducting a performance assessment of each employee to evaluate, motivate and verify in order to improve employee performance. The use of Decision Support Systems (DSS) in human resource management is becoming increasingly important, especially in assessing employees for the selection of chief production employees. Therefore, this research was carried out using the Simple Additive Weighting (SAW) method which has 7 assessment criteria, namely discipline, responsibility, ability, cooperation, thoroughness, leadership and years of service. In the decision making process, testing was carried out on 8 employees in the production department who were alternatives. The results of this research were obtained from calculations using the SAW method starting from determining alternatives, determining criteria and weights, carrying out a normalization matrix, and calculating the final score or ranking results. From the implementation of the SAW method that has been carried out on CV. Wahyu Putra, the recommended candidate for chief employee is alternative A3 with a final value of 0.935. This research provides suggestions on how to determine the head of employees at CV. Putra's revelation, which had previously been carried out subjectively, became objective.
Model Prediksi Penyakit Jantung dengan Penanganan Outlier Menggunakan Interquartile Range dan Extreme Gradient Boosting Azhari, Lukman; Wulandari, Novi; Adiningrat, Feru; Alexander, Allan Desi
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6390

Abstract

Heart disease remains one of the leading causes of death worldwide, with increasing prevalence rates, including in Indonesia. Delayed detection and diagnosis are the main challenges in treating this disease, as most cases are only identified after patients experience serious symptoms or heart attacks. Medical data often containing outliers and noise adds to the complexity of developing accurate predictive models. This study aims to develop a heart disease prediction model using a combination of the Interquartile Range (IQR) method for outlier handling and the Extreme Gradient Boosting (XGBoost) algorithm for predictive modeling. The IQR method is applied at the pre-processing stage to identify and eliminate outliers robustly without reducing data integrity, while XGBoost is used to build an efficient prediction model through an ensemble learning approach. The results showed significant improvements in model performance, with accuracy increasing from 75.41% to 89.47% and AUC-ROC from 0.8615 to 0.9450. The model demonstrates balanced predictive capabilities with precision of 95.24% and recall of 80.00% for cases without disease, and precision of 86.11% and recall of 96.88% for cases with disease. The developed model makes significant contributions by improving data quality through robust outlier handling using the IQR method, building a more accurate prediction model by leveraging the advantages of the XGBoost algorithm in the ensemble learning approach.

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