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INDONESIA
Journal Of Informatics And Busisnes
Published by CV ITTC Indonesia
ISSN : -     EISSN : 29884853     DOI : doi.org/10.47233/jibs
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
The Journal Of Informatics And Busisnes (JIBS) E-ISSN : 2988-4853 is an interdisciplinary journal. It publishes scientific papers describing original research work or novel product/process development. The objectives are to promote an exchange of information and knowledge in research work, and new inventions/developments on the use of Informatics in Economics and Business. This journal is useful to researchers, engineers, scientists, teachers, managers, and students who are interested in keeping a track of original research and development work being carried out in the broad area of informatics in economics and business through a scholarly publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): Juli - September" : 9 Documents clear
Penerapan Metode Apriori Pada Transaksi Penjualan Spare Parts Mobil Maryam Hasan; Sudirman S Panna; Siska Udilawati; Almer Hassan Ali
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3093

Abstract

Until now, commercial vehicle manufacturers continue to innovate their products. One of the manufacturers is PT Nenggapratama Prima Nusantara, engaged in trade and services, namely HINO brand vehicles, spare parts, and services in direct collaboration with PT. Hino Motors Indonesia. The high demand and various types of spare parts certainly drive PT. Nenggapratama Prima Nusantara to maximize existing stock. It aims to ensure that there is no accumulation or shortage of goods. It is important to know the purchasing behavior of customers about which spare parts they buy together. One of the data processing methods usable for this problem is data mining with association analysis using Apriori algorithm. It is a data mining technique that produces rules to determine consumer habits in buying goods simultaneously at once. Based on the results of research using the Apriori method, the largest value (Support x Confidence) is obtained at 0.33. The biggest possibility is that if you buy the Dutro E-4 Fuel Strainer Kit, you will also buy Element Sub Assy Oil with a value of 0.33. Therefore, it can be seen that related spare parts can be arranged simultaneously.
Analisis Dan Perancangan Sistem Informasi Manajemen Aset “Sima” Dengan Menggunakan Qr (Quick Response) Code (Studi Kasus : RSUP Dr. Sitanala Tangerang) Abdullah Muhajir; Eka Yuni Titik Artaningsih
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3161

Abstract

In the current era of the Industrial Revolution 4.0, all sectors implement automation carried out by machines or computerization. Whether it's the industrial sector, health services, information services, and even daily life can not be separated from what is called technology, the Central General Hospital Dr. Sitanala is one of the impacts of the industrial revolution 4.0 where the work process uses a computerized technology system. Hospital assets also require good management, because poor management can lead to a decrease in efficiency and also the quality of service at the hospital. Lack of employee awareness of assets/inventory items makes inventory items, not by initial placement data. With an application-based asset management information system through a QR Code (Quick Response) all Hospital assets are recorded and stored in a data storage (server). QR is a form of evaluation of barcodes that we usually see on a product, with QR all hospital asset data can be seen and traced for their existence so that the optimization of hospital assets goes well.
Analisis Sentimen Pada Twitter Mengenai Pemerintahan Prabowo-Gibran menggunakan metode Linear Regression Hizkia Vincent Hrenysa; Roana, Roana; Elkin Rilvani
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3241

Abstract

This study aims to evaluate the performance of a linear regression model in analyzing sentiment in text data in the form of tweets. The dataset used consists of tweets that have undergone text preprocessing, such as removing URLs, mentions, symbols, and numbers, as well as stemming and tokenization. The purpose of this preprocessing is to improve the quality of the feature representation in the form of TF-IDF, which is used as model input. The evaluation was conducted by comparing the model's performance on raw and cleaned data. The evaluation results show that the linear regression model has a Mean Squared Error (MSE) of 0.1597 and an R² Score of -1.2884, indicating that the model is unable to effectively explain data variability. Visualization of the comparison between predicted and actual scores reinforces this finding, indicating that the model struggles to capture the nuances of informal language, irony, and emotional context in tweets. In conclusion, linear regression is not an ideal approach for text-based sentiment analysis, and the use of contextual representation methods such as word embedding or BERT, along with non-linear predictive models, is recommended for more accurate and relevant results.
Implementasi Data Mining dengan Metode K-Means dan FCM untuk Analisis Pola Pembelian Konsumen Online Rio Rinto Saki; Rizky Juniarko Taruna Putra; Elkin Rilvani
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3106

Abstract

This study explores the implementation of data mining techniques using K-Means and Fuzzy C-Means (FCM) clustering methods to analyze online customer purchasing patterns. The focus of the analysis lies in identifying similarities and segmenting customers based on their transaction behaviors. By using datasets collected from e-commerce platforms during the 2022–2023 period, the study evaluates the effectiveness of each algorithm in discovering meaningful clusters. The results indicate that both methods can group consumers based on purchasing trends, with FCM offering better flexibility due to its fuzzy membership assignment. This clustering approach can support decision-making in targeted marketing, product recommendations, and customer relationship management.
Rancang Bangun Aplikasi Laporan Keuangan Swalayan 89 Berbasis Dekstop Ayu, Dewi Fajar; Rusdiana, Jamal; Rusdiana, Ninanesia
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3295

Abstract

In the digital era, information technology plays a crucial role in enhancing operational efficiency and effectiveness across various sectors, including financial management in retail businesses. Swalayan 89, as a growing retail business unit, still relies on manual recording of financial transactions. This practice may lead to recording errors, delayed reporting, and difficulties in preparing accurate and timely financial statements. This study aims to design and develop a desktop-based financial reporting application using Visual FoxPro 9.0. The application is expected to automate transaction recording processes, improve accuracy, speed up reporting, and support data-driven decision-making in a more efficient and real-time manner. The application is developed using the System Development Life Cycle (SDLC) with the Waterfall model, which includes the stages of Requirement, Design, Implementation, Testing (Blackbox), and Maintenance. Data collection techniques include observation, interviews, and literature study. With the implementation of this application, Swalayan 89 is expected to have a better-organized financial system, minimize recording errors, improve work efficiency, and adapt to the needs of a digitalized system.
Sentiment Analysis of TikTok User Reviews on Google Playstore Using Naïve Bayes Methods Prakoso, Indra; Andhika Aziz Bachtiar; Elkin Rilvani
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3297

Abstract

interactions, one of which is TikTok. The TikTok platform has become a global phenomenon favored by many, especially the younger generation. As the number of users increases, reviews on digital platforms such as the Google Play Store become an important source for understanding users' perceptions of the application. Therefore, a deep understanding of user sentiment toward TikTok is essential for better app development and effective marketing strategies. To analyze TikTok user sentiment, this study employs two well-established computational methods: Support Vector Machine (SVM) and Naïve Bayes. These methods are used to classify user reviews into positive or negative sentiment categories. The approach involves several stages, including data collection, data preprocessing, data splitting, sentiment classification, and model evaluation. The study shows that the SVM model achieved an accuracy of 88.76% with an AUC of 92.61%, outperforming Naïve Bayes, which achieved an accuracy of 84.27% and an AUC of 92.57%. In the positive sentiment category, SVM recorded a precision of 90.74% and a recall of 95.15%, while Naïve Bayes yielded a precision of 83.61% and an almost perfect recall of 99.03%. For negative sentiment, SVM showed a precision of 80.39% and recall of 67.21%, whereas Naïve Bayes had a higher precision of 91.30% but a lower recall of 34.43%, with a lower F1-score of 50%.
Rancang Bangun Aplikasi Pengelolaan Kas Berbasis Web Studi Kasus Rt 9 Kanigaran Dilla Puspita; Yanto, Dwi; Rusdiana, Ninanesia
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3300

Abstract

Financial management at the neighborhood level (RT) plays a crucial role in supporting transparency and accountability in community administration. However, the recording of transactions is still done manually, resulting in slow processes, a high risk of errors, and inefficiencies in preparing monthly and annual financial reports. Transparency is also limited, as financial information is only accessible through community meetings or direct requests. This study aims to design and develop a web-based cash information system to improve the efficiency, accuracy, and transparency of managing incoming and outgoing funds in RT 09, Kanigaran Subdistrict, Probolinggo City. The research applies the Waterfall system development method, which includes requirement analysis, system design, implementation, testing, and maintenance. The system is tested using the black box testing method to ensure each function operates correctly. The developed application includes key features such as multi-access login, master data management (accounts and residents), recording of income and expenses, and the generation of financial reports accessible based on user roles. The testing results show that the application works effectively, supports the digitalization of financial management at the RT level, and enhances transparency. Thus, this system offers an effective solution for improving financial governance at the neighborhood level.
Rancang Bangun Aplikasi Presensi Tutor Tendik PKBM Hidayah Probolinggo Berbasis Web Irwan Hadi Pratama; Lamsadi; Bambang Hariyadi
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3336

Abstract

The development of information technology has driven the digitalization of various aspects of educational management, including attendance systems for educators. At PKBM Hidayah, Kota Probolinggo, attendance recording is still conducted manually, resulting in delays in data recapitulation, potential recording errors, and low efficiency and transparency. This study aims to design and develop a web-based attendance application to improve the efficiency and accuracy of recording the presence of tutors and education staff. The research method used is the SDLC (System Development Life Cycle) model. Data collection techniques employed include observation, interviews, and document studies. For application development, the waterfall method is applied to build the attendance application. This system is expected to provide a modern solution for managing educator attendance within PKBM and other non-formal educational institutions, ensuring better data accuracy, faster processing, and improved transparency in administrative processes.
Text Mining of Trade War in Indonesia News (Tempo.co): A Wordcloud, Sentiment Analysis, and Cluster Sari, Dian Fitriarni; Yasha Langitta Setiawan
Journal Of Informatics And Busisnes Vol. 3 No. 2 (2025): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i2.3422

Abstract

This study focuses on analysing news headlines from Indonesia's leading English daily newspaper, Tempo.co. with total of 4,184 news headlines. We were manually collected it from April to June 2025. The data was processed and analysed using the R Studio package for text mining and sentiment analysis. Various methods such as tokenisation, standardisation, data cleaning, stopword removal, stemming, and lemmatisation were used in the pre-processing stage to extract information. The research methodology included techniques such as wordcloud, sentiment analysis, and clustering to identify the most frequently occurring words, emotional tones, and groups of words that are interrelated in news headlines. Based on the results of the text mining analysis, it was found that the majority of news headlines focused on President Donald Trump's speech on Liberation Day and its impact on trade policy, particularly regarding import tariffs on trading partner countries. This coverage influenced public perception and business decision-making in political and economic aspects.

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