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Journal of Information System, Applied, Management, Accounting and Research
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta
ISSN : 25988700     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
JISIMAR (Journal of Information System, Applied, Management, Accounting and Research), terbit empat kali setahun pada bulan Februari, Mei, Agustus dan November, memuat naskah hasil pemikiran dan hasil penelitian di bidang Teknologi Informasi, Sistem Informasi, Sistem Informasi Manajemen, Sistem Informasi Akuntansi, Ilmu Manajemen dan Manajemen Terapan, Manajemen Sumber Daya, Sistem Manajemen Enterprise, Akuntansi.
Arjuna Subject : -
Articles 758 Documents
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PERBANDINGAN KINERJA METODE STACKING ENSEMBLE DAN NAÏVE BAYES DALAM KLASIFIKASI PENYAKIT ALZHEIMER BERBASIS DATA KLINIS Alya Rafina; Lindy Almadiani; Zuldarmaini Zuldarmaini; Marrylinteri Istoningtyas
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2535

Abstract

Alzheimer's disease is the main reason people develop dementia around the world. However, diagnostic methods that use medical imaging, like MRI scans, are usually costly and not easily available, especially in hospitals and basic healthcare centers. This study is focused on creating and evaluating different classification models using clinical data, and it compares how well the Stacking Ensemble method and the Naïve Bayes method perform. The Stacking Ensemble model uses Random Forest, XGBoost, and Support Vector Machine as the base models, and Logistic Regression acts as the meta-learner.Gaussian Naïve Bayes is also used as the baseline model. The dataset came from Kaggle and includes 2,149 patient records with 32 predictor features after some cleaning and preparation. The data were split into 80% for training and 20% for testing using a stratified method. The model's performance was checked on the testing data and confirmed using 5-fold stratified cross-validation. The experimental results show that the Stacking Ensemble method got an accuracy of 94.65%, precision of 93.88%, recall of 90.79%, and an F1-score of 92.31%. These results do much better than the Naïve Bayes method, which only reached an accuracy of 77.21%. Moreover, cross-validation showed that the proposed model is reliable, achieving an average accuracy of 95.11% for the Stacking Ensemble. These results show that the Stacking Ensemble approach works better and more reliably than the Naïve Bayes model when classifying Alzheimer's disease based on clinical data.
Implementasi Metode Naïve Bayes Dalam Deteksi Akses Tidak Sah Menggunakan Dataset UNSW-NB15 Ahmad Rizeki; Yumi Novita Dewi; Fahrizal Fahrizal; Imam Syafi’i
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2247

Abstract

The rapid growth of information technology and digital activities has led to an increasing number of attacks on computer networks. Various types of cyberattacks, such as unauthorized access and malicious activities, pose serious threats to data security and confidentiality. Therefore, an effective security mechanism is required to detect such attacks, one of which is through the implementation of an Intrusion Detection System (IDS). This study aims to apply the Naïve Bayes algorithm to detect unauthorized access in computer networks. The dataset used in this research is the UNSW-NB15 dataset, which was preprocessed to obtain 82,332 records suitable for classification. The evaluation was conducted using the 10-Fold Cross Validation method with the assistance of RapidMiner software. The experimental results indicate that the Naïve Bayes algorithm achieves excellent performance in classifying network traffic. The proposed model attained an accuracy of 95.96%, a precision of 94.11%, a recall of 98.85%, and an Area Under the Curve (AUC) value of 0.964. The high AUC value demonstrates that the model is highly effective in distinguishing between normal traffic and attack traffic. Based on these findings, it can be concluded that the Naïve Bayes algorithm is a reliable and effective method for intrusion detection systems to enhance network security.
TINJAUAN PUSTAKA SISTEMATIS PERBANDINGAN ALGORITMA K-MEANS DAN DBSCAN PADA KLASTERISASI DATA LAYANAN PUBLIK Sausan Salsabila Musriyanti; Muhammad Fahrury Romdendine; Cakra Trinata
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2560

Abstract

This study presents a systematic literature review comparing K-Means and DBSCAN in clustering public service data. The article adopts the PRISMA approach to make the review process systematic, transparent, and reproducible. Searches were conducted in Google Scholar, ScienceDirect, and IEEE Xplore using keywords related to K-Means, DBSCAN, clustering, and public service data. A total of 11,700 records were identified, 140 studies were retained as candidates after initial screening, and 30 studies were included in the final synthesis. The review shows that K-Means is often used because it is simple, fast, and efficient for structured data, while DBSCAN performs better for datasets containing noise, density variation, and irregular cluster shapes. In public service data, algorithm selection depends on data characteristics, analytical objectives, and parameter sensitivity.
Perceived Usefulness, Trust, and the Effectiveness of Accounting Learning Using ChatGPT Anthonius Anthonius
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2513

Abstract

Artificial Intelligence (AI), particularly ChatGPT, has increasingly been integrated into higher education to support teaching and learning activities. However, the effectiveness of AI-assisted learning depends not only on the availability of the technology but also on students' perceptions of its usefulness and their trust in the technology. This study examines the effects of perceived usefulness and trust on the effectiveness of AI-assisted accounting learning among undergraduate accounting students in Indonesia. A quantitative research design was employed using a survey of 100 undergraduate accounting students who had experience using ChatGPT for learning purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that both perceived usefulness and trust have positive and significant effects on accounting learning effectiveness, with the proposed model explaining 61.4% of the variance (R² = 0.614). The novelty of this study lies in the development of a measurement of AI-assisted accounting learning effectiveness by integrating the Technology Acceptance Model (TAM) and Trust Theory within the context of generative AI. The findings provide practical implications for higher education institutions and AI developers in designing AI-assisted learning environments that enhance students' perceived usefulness, trust, and accounting learning effectiveness.
PERANCANGAN SISTEM PRESENSI BERBASIS WEB MENGGUNAKAN ALGORITMA HAVERSINE PADA PT MITRA PERKASA EKATAMA Isma Raudhatul Janah; Dani Yusuf; Siti Setiawati; Hendarman Lubis
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2576

Abstract

The development of information technology encourages companies to implement digital systems to support administrative management, including employee attendance processes. PT Mitra Perkasa Ekatama still uses fingerprint machines that have limitations in monitoring the attendance of employees who work outside the office or project locations and do not yet support attendance location validation. This study aims to design a web-based employee attendance system by implementing the Haversine Algorithm as a method for calculating the distance between the user's location and the company's location to validate attendance. The system development method used is Waterfall which includes the stages of planning, needs analysis, design, implementation, testing, implementation, and system maintenance. The system was built using PHP, HTML, CSS, JavaScript, MySQL, and utilizes Global Positioning System (GPS) technology. The Haversine Algorithm is used to calculate the distance based on latitude and longitude coordinates so that the system can determine whether the user is within the permitted attendance radius. The results of the study show that the system successfully integrates login features, employee data management, positions, departments, attendance entry, attendance departure, permit applications, leave applications, and attendance recapitulation in one web-based application. Testing using the Black Box Testing method shows that all system functions run well with a success rate of 100%. The resulting system is able to help companies in automatically validating attendance locations and improving the management of employee attendance data in an integrated manner
Penerimaan Siswa Sekolah Berbasis Jarak Menggunakan Algoritma Haversine Dani Yusuf; Hendarman Lubis; Uus Rusmawan
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2547

Abstract

New student admission is a critical process in the education system that requires fairness, objectivity, and transparency, particularly in domicile-based selection mechanisms. This study aims to design and implement a distance-based student admission system using the Haversine algorithm at SMK Metland Cibitung. The research method applies an applied quantitative approach with a system development framework utilizing geographic coordinate data (latitude and longitude) to calculate the distance between applicants’ residences and the school location. The Haversine algorithm is used to compute distances based on the great-circle distance concept, providing higher accuracy compared to conventional estimation-based approaches. The results show that the system is able to automatically calculate distances and rank applicants based on proximity with consistent outcomes. The implementation improves selection efficiency and supports the principles of objectivity and transparency in the Student Admission System (SPMB). Therefore, the Haversine algorithm is an effective solution for developing a distance-based student admission system.
Environmental Accountability or Symbolic Reporting? and Evidence from ESG Disclosures in Indonesia’s Telecommunications Industry Evy Steelyana; Ghassani Ghassani; Fransiska Priska; Kireyna Tamardandika; Michael Dimas Prasetyo
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2356

Abstract

This study examines the credibility of Environmental, Social, and Governance (ESG) environmental disclosures by evaluating whether reported sustainability information reflects actual environmental performance in Indonesian telecommunications companies. Using qualitative content analysis of sustainability reports from major telecom firms between 2022 and 2024, the study assesses disclosure transparency, availability of quantitative indicators, and alignment between narrative reporting and measurable outcomes. The findings reveal substantial discrepancies between disclosed commitments and verifiable performance evidence. Although all companies report environmental initiatives, the majority of disclosures are narrative-driven and lack quantitative indicators that enable objective performance assessment. Paper reduction initiatives associated with digitalization provide the most consistent measurable outcomes, while programs addressing electronic waste, recycling, and environmental awareness are typically reported without clear metrics. These patterns suggest that ESG reporting may function not only as an accountability mechanism but also as alegitimacy tool aimed at maintaining stakeholder confidence. From a sustainability accounting perspective, the results highlight a gap between disclosure volume and information reliability. The absence of standardized metrics limits comparability across firms and reduces the decision usefulness of ESG information for investors and regulators. The study also indicates that initiatives integrated into core business operations are more likely to produce measurable outcomes and credible disclosures. This research contributes to sustainability accounting literature by shifting the focus from the existence of environmental programs to the quality and credibility of ESG disclosures. The findings provide implications for regulators, standard-setting bodies, and corporate managers seeking to enhance transparency, accountability, and the effectiveness of sustainability reporting in infrastructure-intensive industries.
PENGARUH MOTIVASI KERJA, DISIPLIN KERJA DAN KOMPETENSI KERJA TERHADAP PRODUKTIVITAS KERJA KARYAWAN Sita Dewi; Ronaldo Sarco; Dwi Listyowati; Diana Setyo Dewi; Kuncu Saragih
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2561

Abstract

The retail sector in Indonesia is growing rapidly and is highly competitive; one example is Miniso, which operates 88 stores. To survive and more develope, businesses must improve their performance and productivity. Employees, as the company’s most valuable asset, are the driving force behind achieving high productivity, thereby ensuring the company’s goals are met. Factors that influence work productivity include work motivation, work discipline, and work competence. These three factors are fundamental elements that require serious attention from management. Work motivation is an internal or external drive that makes individuals more enthusiastic and innovative in their work, thereby increasing work productivity. Work discipline reflects an individual’s commitment and sense of responsibility in performing their duties. With strong discipline, employee performance improves, work quality increases, and productivity rises as a result. Job competencies refer to the abilities possessed by employees in the form of knowledge, skills, and work attitudes. Competencies are a key determinant of the quality of an employee’s work; strong competencies lead to strong performance, which in turn results in high productivity. This study aims to determine the influence of work motivation, work discipline, and work competence on employee performance. This study is a case study of Miniso employees at Central Park Mall in West Jakarta, with a sample size of 30 employees. The results show that, individually, work motivation, work discipline, and work competence significantly influence the work productivity of Miniso Central Park employees. Simultaneously, work motivation, work discipline, and work competence also influence the work productivity of Miniso Central Park employees.
ANALISIS KOMPARATIF KINERJA LOGISTIC REGRESSION DAN RANDOM FOREST BERBASIS TF-IDF UNTUK KLASIFIKASI SENTIMEN KOMENTAR TIKTOK TERHADAP PROGRAM MAKAN BERGIZI GRATIS Febriza Evan Nugraha; Ibni Faiq Athallah; Marrylinteri Istoningtyas
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2515

Abstract

Sentiment analysis of public policy on social media has become increasingly important as public participation in digital spaces continues to grow. This study compares the performance of Logistic Regression and Random Forest algorithms based on TF-IDF for classifying the sentiment of TikTok comments regarding the Free Nutritious Meal Program into three classes: positive, neutral, and negative. The dataset consists of 4,811 public comments collected from six TikTok videos between January and June 2026. After preprocessing and manual labeling, 3,884 valid comments were obtained with a sentiment distribution of 39.73% negative, 37.97% positive, and 22.30% neutral. Class imbalance was addressed using SMOTE on the training data, and the dataset was split using an 80:20 stratified split. Evaluation results show that Logistic Regression outperformed Random Forest across all metrics, achieving an accuracy of 0.76 and a macro F1-score of 0.74 compared to Random Forest's accuracy of 0.73 and macro F1-score of 0.71. In both models, the neutral class consistently showed the lowest performance, indicating semantic ambiguity that cannot be optimally captured by frequency-based feature representations. This study provides empirical evidence that Logistic Regression is more suitable for Indonesian social media text sentiment classification with TF-IDF representation, and recommends exploring context-based models such as IndoBERT for future research.
ANALISIS DIVIDEND PAYOUT RATIO DAN DEBT TO EQUITY RATIO TERHADAP KINERJA KEUANGAN PERUSAHAAN FARMASI YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) TAHUN 2021 – 2023 Titi Aslah; Endar Julianah; Anisa Nabila
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2620

Abstract

This study analyzes the effect of the Dividend Payout Ratio (DPR) and Debt to Equity Ratio (DER) on the financial performance of pharmaceutical companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2023 period. The population consists of 13 companies, with a sample of 11 companies meeting the criteria. This research employs a quantitative approach with a documentation technique, collecting secondary data from annual financial reports published on www.idx.co.id. Data analysis was conducted using classical assumption tests, multiple linear regression, correlation analysis, t-test, and F-test with the assistance of SPSS version 20. The results indicate that DPR does not significantly affect Return on Equity (ROE), with a t-value of -1.083 < t-table 2.04227 and a significance level of 0.287 > 0.050. An increase in DPR tends to decrease ROE, as dividends reduce reinvested earnings. Meanwhile, DER has a negative effect on ROE, with a t-value of -18.039 > t-table 2.04227 and a significance level of 0.000 < 0.050. High reliance on debt can lower profitability and increase financial risk. Simultaneously, DPR and DER significantly affect ROE, with an F-value of 168.456 > F-table 3.32 and a significance level of 0.000 < 0.05. DPR reflects profit distribution policies, while DER influences capital structure, providing insights into corporate financial management strategies to maximize profitability.

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