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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 795 Documents
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Penerapan Geographic Information System (GIS) Menggunakan Metode GeoJSON untuk Visualisasi Data Geospasial Sebaran UMKM Batik Al'Amin, M; Naufal, Abdul Razak; Suseno, Akrim Teguh
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.v6i2.5977

Abstract

Pekalongan City is one of the cities where batik grows. Until now, there are thousands of batiks MSMEs spread across many points in Pekalongan. To introduce batik MSMEs to the general public, a WebGIS-based platform is needed as an information center for Pekalongan batik. This research aims to build a website-based Geographic Information System (GIS) using GeoJSON as a platform for mapping the distribution of MSMEs of Batik and a digital batik literacy center in Pekalongan City. The GeoJSON method is used because it has the ability to read population data in only 14.7 seconds, loading geographic data via the GIS REST API usually takes 38.4 seconds, this makes it very suitable for use in web applications. Its simplicity, compatibility and platform independence have contributed to the geospatial community. GeoJSON is a natural fit with modern web technologies such as JavaScript, which makes it easy to use in web application development and integrates with web service APIs. The test scenario results obtained a total score of 85.8 and the System Usability Scale (SUS) test results obtained a total score of 83.5, which places it in the very good category. The SUS rating scale indicates that potential users find this application easy to use. The design of this application based on test results shows a better effect in terms of ease of use, apart from that, potential users generally give positive marks, so it can be concluded that the implementation of WebGIS using the GeoJSON method for visualizing the distribution of Batik MSMEs can be implemented very well.
Sentiment Analysis of Multilingual Customer Reviews in the Hospitality Sector Puspitarini, Titis
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.v6i2.6680

Abstract

This study investigates the role of sentiment analysis in improving service quality and guest satisfaction in the hospitality industry. Recognizing the increasing importance of customer feedback in shaping operational strategies, the research utilized a dataset of 1,141 original hotel reviews, comprising 1,113 positive and 28 negative sentiments. The methodology employed IndoBERT for sentiment classification, supported by a series of preprocessing steps, including text normalization, stopword removal, and text length filtering, to ensure data integrity. To address the significant class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, resulting in a balanced dataset of 1,923 reviews, with 1,450 positive and 473 negative sentiments. The model demonstrated strong performance, achieving an Area Under the Curve (AUC) score of 94.7%, highlighting its capability to classify sentiments accurately. Findings reveal that positive sentiments often focus on room quality, staff friendliness, and breakfast service, while negative feedback highlights service delays and cleanliness issues. These insights enable data-driven recommendations for improving guest experiences and addressing critical concerns. The study demonstrates the potential of sentiment analysis as a strategic tool for enhancing service delivery, fostering guest loyalty, and maintaining competitiveness in the hospitality market.
Implementasi Model Sistem Dinamik Terhadap Analisis Tingkat Kemacetan Lalu Lintas Lubis, Irsyad Baihaqi; Syahputra, M.R; Syawaluddin, Syawaluddin; Sitorus, Syahriol
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.v6i2.6683

Abstract

Overcoming existing traffic congestion is something that many people hope for, especially people who are always trapped in these traffic problems. One of the methods used to reduce traffic congestion is the dynamic system approach. This method analyses and simulates the problem by determining the causal variables. The analysis was conducted using a causal loop diagram model which was then simulated with a stock flow diagram model using Ventana Simulation (Vensim) software. The results showed that there was a decrease in the average level of traffic congestion on Jalan Setia Budi with an initial condition of 1.06% to 0.71%. This 32% decrease has an impact on traffic conditions that are busy without any delays in travel time on Jalan Setia Budi.
Komparasi Metode Random Forest Dan Support Vector Machine (SVM) Untuk Pemodelan Klasifikasi Serangan DDos Lauwl, Christoper Michael; Husain, Husain; Nuzululnisa, Baiq Nadila; Wijaya, Hartono
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.v6i2.6684

Abstract

The Distributed Denial of Service (DDoS) attack is a type of cyberattack that aims to render a service, network, or website inaccessible to legitimate users. This attack not only disrupts services but also causes server crashes by repeatedly sending data packets, commonly referred to as spam. DDoS attacks can be identified as traffic anomalies. The National Cyber and Crypto Agency (BSSN) recorded 403,990,813 traffic anomalies with 347 cases specifically attributed to DDoS attacks. Based on this issue, a model capable of classifying DDoS attacks is necessary. This study employs the Random Forest and Support Vector Machine (SVM) methods through the steps of data collection, dataset loading, data preprocessing, classification modeling, and performance evaluation. In the final stage, the best method between Random Forest and Support Vector Machine is determined. The results indicate that Random Forest achieved an accuracy of 99.9%, whereas Support Vector Machine obtained an accuracy of 97.0%. Therefore, it can be concluded that Random Forest demonstrates better accuracy in classifying DDoS attacks.
Penerapan Program Dinamik dalam Menentukan Rute Optimal Perjalanan dari Pematangsiantar Menuju Samosir Simarmata, Linda Puspa Ayu; Sitanggang, Intan; Andini, Mira; Sirait, Barthy Ladi C.
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.v6i2.6688

Abstract

A tourist area with many roads often makes it difficult for someone to determine the optimal route, both in terms of distance or costs incurred from the place of origin to the destination. Moreover, in determining a decision support system to determine the route of a tourist trip is needed to determine the optimal route so that the costs incurred are minimum. To solve the problem in this article, we use the dynamic programming method where the method used is the forward method. With the aim of determining the fastest distance from Siantar to Samosir so that the minimum costs incurred. Dynamic Program (dynamic programming) is a method of solving problems by decomposing the solution into a set (stage), such that the solution to the problem can be viewed as a series of interrelated decisions. The solution used is dynamic programming with the data that has been obtained then processed and calculated so that the results obtained are optimal, It is known from the results of the calculation analysis, it can be concluded that the shortest route for tourist trips from Siantar to Samosir, namely from Siantar then to Sidamanik, then to Tomok, continuing to Simanindo, then to Pangururan until finally to Tele with a distance of about 116,2 km /h.
K-Means Clustering Untuk Mengukur Pengaruh Kompetensi Terhadap Kinerja Pegawai Syaqila, Saidatus; Fakhriza, M.
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.v6i2.6758

Abstract

Human resources play an important role in improving organizational performance, including in the North Sumatra Province Youth and Sports Agency (Dispora). This study aims to measure the effect of competence on employee performance using the K-Means Clustering algorithm, known as an unsupervised data clustering method. The dataset consists of 700 employee data with 15 attributes covering technical, managerial, and social competencies. Data were collected through direct surveys and processed using Python with a normalization process through the StandardScaler method to ensure data consistency. The elbow method was used to determine the optimal number of clusters, resulting in five clusters: best performance, very good, and average. The results of the analysis show that the clustering results group employees into five clusters, namely Cluster 0 with 145 employees who have high technical competence, Cluster 1 with 160 employees who excel in social and managerial competence, Cluster 2 with 125 employees who show average competence in all aspects, Cluster 3 with 135 employees who have moderate technical competence but excel in social competence, and Cluster 4 with 135 employees who have great potential for development. This research provides practical benefits in the form of identifying competency patterns for developing group-based training needs, as well as more objective strategic decision-making in human resource management. Thus, this research is expected to support improving employee performance through an effective data-based approach.
Analisis Sentimen Terkait Hilirisasi Industri Pada Opini Masyarakat X dengan Menggunakan Naive Bayes Pratama, Aditya Budi; Febriawan, Dimas
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.v6i2.6795

Abstract

This research examines public sentiment toward Indonesia's industrial downstreaming policy using data sourced from X. The study employs the Naive Bayes algorithm to categorize public opinions into three sentiment types: positive, negative, and neutral. Data collection was conducted via a crawling process utilizing the X API and tools like Tweepy, followed by preprocessing steps such as data cleansing, tokenization, case normalization, stopword removal, and either stemming or lemmatization. Subsequently, the data was manually annotated using a lexicon-based sentiment method to ensure accurate classification. The findings reveal that the Naive Bayes algorithm achieved an accuracy rate of 81.75% in sentiment classification, with the highest performance observed in identifying positive sentiments. This research offers valuable insights into public perspectives on the industrial downstreaming policy and suggests recommendations for policymakers to develop strategies that better resonate with public sentiment. Leveraging X as a data source allows for real-time analysis that adapts to shifts in public opinion.
Klasfikasi Tingkat Kematangan Roasting Biji Kopi Berbasis Deep Learning dengan Arsitektur MobileNet Firmansyah, Tegar; Kurniawan, Rudi; Hidayat, Asep Toyib
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.v6i2.6811

Abstract

Coffee is one of the most widely consumed beverage ingredients in Indonesia and has high economic value to improve the community's economy and as a source of foreign exchange. The roasting process is an important stage in coffee processing because it affects the aroma and flavor of coffee. What is often encountered is that visually determining the level of coffee roasting is often inaccurate and prone to human error. To overcome this problem, this study uses a deep learning approach with a transfer learning method based on MobileNet architecture to classify the level of coffee roasting maturity based on digital images. MobileNet was chosen due to its lightweight and fast architecture, suitable for implementation on mobile devices. This research aims to compare the performance of the model in detecting coffee roasting level automatically, efficiently, and objectively. With this approach, it is expected that coffee enthusiasts and producers can easily recognize the type of coffee roasting, support product quality consistency, and reduce dependence on experts in the roasting process. This study analyzed the performance of the classification model with the results showing excellent performance. The model achieved a total accuracy of 99.50%, with consistently high precision, recall, and f1-score values across all classes, including several classes with perfect scores (1,000). Evaluation using ROC curves and AUC also demonstrated the model's ability to distinguish between the two classes.
Perbandingan Algoritma K-Nearest Neighboor dan Naive Bayes Dalam Prediksi Penyakit Ginjal Kronis Pada Lansia Simbolon, Marco Duran; Bukit, Dimas Dimanta; Purba, Rian Elby; Ketaren, Faisal Haries; Prabowo, Agung
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.v6i2.5938

Abstract

Chronic kidney disease is a serious illness that requires early diagnosis to improve treatment outcomes, especially in the elderly. The main challenge in diagnosing this disease lies in the fact that symptoms often do not appear until the disease has reached an advanced stage, which necessitates the use of accurate prediction methods. Additionally, the dataset's limited size, consisting of only 195 patient records, may affect the algorithm's ability to identify patterns. Choosing the appropriate algorithm is also a challenge, as some algorithms have limitations in handling complex medical data. This study aims to evaluate the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting chronic kidney disease. The dataset was analyzed using Weka Waikato software and tested using the 9-fold cross-validation method. The best results were obtained using the Naïve Bayes algorithm, with an accuracy of 97.4359%. Based on these results, it can be concluded that both algorithms can be used to predict chronic kidney disease in the elderly. However, to further improve prediction accuracy, proposed solutions include expanding the dataset with more diverse data and optimizing the algorithm's hyperparameters. On the other hand, the Naïve Bayes algorithm demonstrated higher accuracy compared to KNN in this study, making it the more recommended choice.
Aplikasi Identifikasi Gaya Bahasa Sarkasme Dalam Lirik Lagu Berbasis Mobile Menggunakan Support Vector Machine Algoritma Masengi, Julio Joseph Victor; Frans, Rycko Giovann Leon; Taju, Semmy Wellem
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6103

Abstract

In the digital era, with the widespread use of social media, the use of sarcasm in song lyrics often presents a unique challenge in the interpretation process. One issue is the difficulty in detecting sarcastic language due to its implicit nature and dependence on context. Conventional methods often fail to capture this complex language pattern, which may lead to misunderstandings. This research aims to develop a mobile-based application capable of identifying sarcasm in song lyrics using the Support Vector Machine (SVM) algorithm. The application is designed to detect sarcasm in song lyrics, which is often hard to identify accurately through traditional methods. The development process includes several stages, such as data collection, pre-processing song lyrics data, applying the Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature extraction. A sarcasm keyword dataset containing 600 data points with sarcasm elements and a general song lyrics dataset without sarcasm elements were collected and used for machine learning model training. The processed data is then classified using Support Vector Machine (SVM), which categorizes the analysis results into two main categories: sarcasm and non-sarcasm. The proposed classification model demonstrates performance with an Accuracy of 98.14%, Sensitivity of 96.13%, Specificity of 100%, and MCC of 0.9645, indicating a strong ability to distinguish between sarcastic and non-sarcastic language. This research aims to enhance users' understanding of song lyrics, especially on social media, to reduce misunderstandings related to sarcasm. It is hoped that this research can contribute to the development of technology for understanding sarcastic language in song lyrics.

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