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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 20 Documents
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Search results for , issue "Vol. 3 No. 4 (2026): Januari - Maret" : 20 Documents clear
Prediksi Kualitas Udara DKI Jakarta Menggunakan Algoritma Random Forest Berbasis Time-Lag Feature Prasetya, Heronimus Diego; Pratama, Jeremia Sandy; Khoirunnisaa, Alifah; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3933

Abstract

The volatility of air quality in Jakarta, which often deteriorates abruptly, demands a proactive early warning mechanism rather than mere real-time monitoring. A major limitation in environmental datasets is the class imbalance, where extreme hazardous conditions are recorded much less frequently than normal conditions, causing them to be overlooked by standard prediction models. This study aims to develop an H+1 (next-day) air quality prediction system by integrating the Random Forest algorithm with the SMOTE (Synthetic Minority Over-sampling Technique) data balancing technique. A Time-Lag feature engineering approach was applied to transform historical data from 2010-2025 into future predictive variables. Experimental results demonstrate that the application of SMOTE successfully improved the model's sensitivity in recognizing 'Unhealthy' categories that were previously difficult to detect. Feature analysis revealed that the accumulation of surface Ozone (O3) and Particulate Matter (PM10) serve as the most dominant indicators triggering air status changes for the following day. This system is intended to serve as a health mitigation reference for the public prior to outdoor activities.
Analisis Audit Sistem Informasi Pada PT XYZ Menggunakan Framework Cobit 2019 Syakirah, Muthia; Riadi, Safina; Pratama, Dicky
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3936

Abstract

Information system audit is essential to ensure the effectiveness, security, and alignment of IT systems with a company’s strategic objectives. This study analyzes PT XYZ’s information system using the COBIT 2019 framework, focusing on process capability assessment, governance compliance, and identifying areas for improvement. The research employs a descriptive case study approach, using primary data from interviews and observations, and secondary data from internal documentation and company SOPs. Analysis covers five COBIT 2019 domains (EDM, APO, BAI, DSS, MEA) and maps processes to Enterprise Goals to evaluate strategic alignment. Audit results indicate most domains are at Level 2 (Managed), while BAI and MEA remain at Level 1 (Performed), highlighting the need for improved documentation and monitoring. Recommendations include enhancing process documentation, strengthening IT governance, staff training, implementing SLAs, and conducting regular evaluations. This study provides a foundation for PT XYZ to improve information system capability and IT governance effectiveness.
Peran Good Corporate Governance Dalam Mencegah Kecurangan (FRAUD) Pada Industri Perbankan Zein, Ahmad Wahyudi; Balqis, Keisya Putri; Nasution, Alya Arianti; Harahap, Indah Tri Sari
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3942

Abstract

The banking industry has a strategic function when supporting financial stability, so its management is required to be held openly and accountably. The implementation of Good Corporate Governance as a framework is necessary when strengthening public trust and reducing the potential for the presence of fraudulent practices. This article aims to understand the role of Good Corporate Governance in fraud prevention efforts in the banking sector. The method used is a qualitative descriptive approach by reviewing various academic literature including scientific journals and relevant books. The results of the discussion show that the principles of Good Corporate Governance have a role in strengthening the internal control system, increasing accountability, and creating a culture of organizational ethics. The conclusion of the article emphasizes that Good Corporate Governance is a strategic instrument in building banking governance with health, integrity, and sustainability in order to maintain public trust and encourage the stability of the national financial system. This approach also shows a conceptual overview with the overall importance of governance, supervision, compliance, and organizational responsibility when reducing the risk of banking fraud in a systematic and sustainable manner.
Optimasi Support Vector Machine Menggunakan Feature Selection Chi-Square untuk Klasifikasi Sentimen Program Makan Siang Gratis Darmawan, I Komang; Prayogo, Aji; Chan, Steven; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3943

Abstract

The Free Nutritious Lunch program policy has triggered massive public discourse on social media X, reflecting diverse public perceptions toward government policy effectiveness. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm by implementing Chi-Square Feature Selection to address high data dimensionality and noise challenges in social media text. A dataset of 10,524 tweets was acquired and processed through preprocessing, TF-IDF weighting, and lexicon-based automatic labeling. The results show that Chi-Square feature selection integration successfully reduced dimensions from 16,394 to the 1,000 best features without degrading accuracy. The linear kernel SVM model achieved an optimal accuracy rate of 91.12%. However, this study identifies that this high accuracy is heavily influenced by the dominance of the positive class, whereas performance on the negative and neutral classes remains limited due to data imbalance. Overall, feature optimization proved to increase computational efficiency while maintaining accuracy stability in mapping public responses to strategic national policies.
Implementasi Metode Kimball dan Pendekatan Star Schema dalam Membangun Data Warehouse Analisis E-Commerce Oktarina, Theresia; Sanjaya, Aloisius Egi; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan platform e-commerce mendorong peningkatan volume data transaksi yang sangat besar dan kompleks. Namun, data tersebut umumnya masih tersimpan dalam sistem operasional yang belum optimal untuk kebutuhan analisis jangka panjang dan pengambilan keputusan strategis. Penelitian ini bertujuan untuk merancang dan mengimplementasikan data warehouse pada platform e-commerce VWX, khususnya pada kategori Pet Supplies, guna mendukung analisis bisnis secara multidimensi. Metode yang digunakan adalah pendekatan Kimball dengan model star schema, yang terdiri dari satu tabel fakta dan dua tabel dimensi. Data diperoleh dari hasil web scraping dan diproses melalui tahapan ETL (Extract, Transform, Load) menggunakan RapidMiner untuk memastikan kualitas, konsistensi, dan kesiapan data. Selanjutnya, data dianalisis menggunakan teknik OLAP seperti roll-up, drill-down, slice, dan dice untuk menggali informasi terkait performa produk, kategori, dan kualitas rating pelanggan. Hasil penelitian menunjukkan bahwa penerapan data warehouse dengan model star schema mampu menyajikan data secara terstruktur dan mempermudah proses analisis, sehingga menghasilkan informasi yang relevan dan dapat dimanfaatkan sebagai dasar pengambilan keputusan yang lebih efektif pada platform e-commerce VWX.
Implementasi Data Warehouse Skema Snowflake untuk Analisis Determinan Kompetensi Siswa Samuel Dimas Sutikno; Michael Imanuel; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The implementation of the National Assessment (AN) produces complex educational data volumes, covering the results of the Minimum Competency Assessment (AKM), Character Surveys, and Learning Environment Surveys. The management of transactional and scattered data often hinders the comprehensive education quality evaluation process. This study aims to design and implement a Data Warehouse using the Snowflake Schema method to analyze the influence of socio-economic status and school profiles on student literacy and numeracy achievements. Kimball's Nine-Step Methodology approach is used in data architecture design. Test results show that the Snowflake scheme is effective in handling regional and school dimension hierarchies by reducing storage redundancy. OLAP (Online Analytical Processing) analysis reveals significant gaps in literacy and numeracy scores based on school accreditation levels and student economic backgrounds, where school quality is proven to be a moderating variable in improving student achievements from low economic groups.
Implementasi Data Mining Dalam Mengkategorikan Produk Terlaris dan Kurang Laris Pada Toko Retail OPQ Menggunakan Metode Naive Bayes Oktarina, Theresia; Ketut Agus Wiikananda; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

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Abstract

Pesatnya volume data transaksi menuntut efisiensi dalam pengelolaan stok dan rencana pemasaran di industri ritel. Riset ini mengevaluasi penggunaan algoritma Naive Bayes untuk memisahkan produk di Toko OPQ menjadi kategori "Unggulan" dan "Reguler". Dengan bantuan RapidMiner Studio, dataset diproses melalui fase pembersihan, standarisasi Z-score, serta pengujian dengan rasio data 70:30. Temuan eksperimen menunjukkan akurasi model mencapai 99%. Meski demikian, ditemukan kendala pada nilai presisi kelas "Unggulan" yang hanya sebesar 16,67% akibat adanya ketimpangan distribusi jumlah sampel. Studi ini menyimpulkan bahwa metode ini efektif untuk memetakan tren, namun memerlukan optimasi pada keseimbangan dataset
Analisis Sentimen Berita terhadap Harga Saham BBCA Menggunakan Naive Bayes Michael Imanuel; Samuel Dimas Sutikno; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The dynamics of the Indonesian capital market are increasingly influenced by information flow in the digital era. PT Bank Central Asia Tbk (BBCA), as a key market proxy, experienced price volatility in 2024–2025 despite solid fundamentals, indicating the influence of market psychology. This study aims to analyze the effect of stock market news sentiment on BBCA stock prices and test the effectiveness of the Multinomial Naive Bayes algorithm. Using a text mining approach, 5,000 economic news articles (2020–2025) were processed using TF-IDF and classified into positive, negative, and neutral sentiments. The results show the model achieved 92.4% accuracy with 89% precision for negative sentiment detection. Pearson correlation analysis revealed a strong positive relationship (r = 0.78) between daily sentiment scores and the following day's closing prices. The study concludes that news sentiment is a valid leading indicator for stock movements. The Naive Bayes algorithm proved efficient for financial text analysis, offering a viable tool for investor risk mitigation.
Pengaruh Gaya Kepemimpinan Transformasional, Lingkungan Kerja Digital, Dan Kompetensi Karyawan Terhadap Kinerja Karyawan Dengan Motivasi Kerja Sebagai Variabel Mediasi Saputra , Rizki
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3972

Abstract

This study aims to analyze the effect of transformational leadership style, digital work environment, and employee competence on employee performance, with work motivation as a mediating variable. This research is motivated by the increasing demands of the digital era, which require organizations to adopt adaptive leadership, develop technology-based work environments, and enhance employee competence in order to improve performance sustainably.This study employs a quantitative approach using a survey method. The population of this research consists of employees at the selected company, with a saturated sampling technique applied. Data were collected through questionnaires developed based on indicators of each research variable. The collected data were analyzed using statistical techniques through SPSS, including validity and reliability tests, classical assumption tests, multiple linear regression analysis, coefficient of determination (R²), and mediation testing using the Sobel method.The results indicate that transformational leadership style, digital work environment, and employee competence have a positive and significant effect on employee performance. Furthermore, work motivation also has a positive and significant effect on employee performance. The mediation test results reveal that work motivation significantly mediates the relationship between transformational leadership style, digital work environment, and employee competence on employee performance. The coefficient of determination indicates that a substantial proportion of the variation in employee performance can be explained by the variables examined in this study, while the remaining variation is influenced by other factors outside the research model.This study is expected to provide both theoretical and practical contributions by offering insights for organizations to enhance employee performance through strengthening transformational leadership, improving digital work environments, developing employee competence, and managing work motivation effectively.
Analisis Sentimen Ulasan Aplikasi Bibit Menggunakan TF-IDF dan Support Vector Machine Adeodatus, Marselinus Dewadaru Bayu; Nopitasari, Sepiyana; Meivia, Wirda Arta; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

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Abstract

The rapid growth of financial technology (fintech) applications has increased the number of user reviews on digital platforms. These reviews contain valuable information regarding application quality, yet they are unstructured and difficult to analyze manually. This study aims to classify user review sentiments of the Bibit investment application into positive and negative categories using the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm. The dataset was obtained from Kaggle, consisting of user reviews of the Bibit application collected from Google Play Store. The data were processed through several preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and classification was conducted using SVM with a linear kernel. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the combination of TF-IDF and SVM provides good performance in classifying the sentiment of Bibit application user reviews.

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