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Muhammad Wali
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INDONESIA
Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Published by STMIK Indonesia Banda Aceh
ISSN : 27768074     EISSN : 27237079     DOI : https://doi.org/10.35870/jimik
Core Subject : Science, Education,
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science
Jurnal Indonesia: Manajemen Informatika dan Komunikasi is a scholarly publication dedicated to advancing the fields of information technology and communication management in Indonesia. The journal serves as a platform for researchers, academicians, practitioners, and policymakers to share their insights, knowledge, and expertise in these domains. This journal is a peer-reviewed online journal dedicated to high-quality research publications focused on research, implementation. Jurnal Indonesia: Manajemen Informatika dan Komunikasi is a scholarly publication dedicated to advancing the fields of information technology and communication management in Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 782 Documents
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Analisis Representasi Konflik Keluarga dalam Film Perayaan Mati Rasa (2025) Averil Sasha Audrey; Dian Hutami Rahmawati
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i2.1883

Abstract

This study aims to analyze the representation of family conflict in the film Perayaan Mati Rasa using John Fiske's semiotic analysis through the levels of reality, representation, and ideology. This research employed a qualitative descriptive approach with a postpositivist paradigm. Data were collected through observations of scenes, dialogues, expressions, and visual elements related to family conflict. The results show that family conflict is represented through sibling rivalry, emotional neglect, silent treatment, authoritarian parenting, generational gaps, economic pressure, and differences in expectations between parents and children. At the reality level, conflict is shown through disappointed expressions, avoidant gestures, and silence. At the representation level, the film constructs conflict through closed communication and unequal power relations within the family. Meanwhile, at the ideological level, the film criticizes patriarchal culture, the expectation that the eldest child must always be strong, and the assumption that family is always a safe space for its members. This study concludes that family conflict in the film arises from failures in emotional communication, lack of empathy, and unequal family relationships.
Implementasi Sistem Data Mining Penerimaan Siswa Baru Menggunakan Metode Naive Bayes Nurul Khoiriyah; Veri Arinal
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2015

Abstract

New student admission is a pivotal initial phase in sustaining educational operations at MTs NU Islamiyah, Tangerang, Banten. Although the institution enrolls students annually, fluctuations between registered and unregistered applicants present significant operational challenges, particularly in institutional policy planning and the optimal allocation of learning infrastructure. This study aims to build a classification model to predict new student admission status using the Naive Bayes method implemented in Python. Predictions were conducted by analyzing historical student attributes, including gender, family economic status, and residential distance to the school. The analyzed dataset comprised 623 training instances and 82 testing instances. The algorithm calculated posterior probabilities for each category based on historical records. The testing results demonstrated that the model achieved strong classification performance for the admitted class, yielding a precision of 87%, a recall of 100%, and an f1-score of 93% (support: 71). Conversely, the model showed poor performance in predicting the non-admitted class, resulting in 0% precision, recall, and f1-score (support: 11).
Pengembangan Sistem Informasi Prediksi Penjualan Kue Menggunakan Metode Weighted Moving Average pada Toko Kue Nyonya Yati, Jakarta Selatan Wewisman Taofanao; Kiki Setiawan; Mesra Betty Yel
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2019

Abstract

This study aimed to develop a web-based sales prediction information system using the Weighted Moving Average (WMA) method at Nyonya Yati Cake Shop, South Jakarta. The shop recorded and managed sales data manually, which made it difficult to retrieve historical sales information and left it without a calculation-based means of estimating sales for the next period. The study used a descriptive qualitative approach, with data collected through observation, interviews, and a literature review. The system was developed following the Waterfall model, which comprises requirements analysis, design, implementation, testing, and maintenance, and was built as a website using PHP, MySQL, XAMPP, HTML, CSS, and JavaScript. It provides product, stock, sales, user account, reporting, and forecasting features. The WMA method assigns different weights to historical observations, with more recent sales data given greater influence. In the calculation example for Cheesecake, sales data from January to December were used with a forecasting configuration of MA=3. The forecasts were 541 pcs for April, 542 pcs for May, and 541 pcs for June, and the forecast for January of the following period (t = 13) was 540 pcs. Forecast errors were evaluated using the tracking signal, which had a final value of −3.000. The system organizes sales data in a structured way, and its forecasts can serve as a consideration for stock, production, and sales planning.
Penerapan Microservices dengan Docker Swarm pada Sistem Informasi Manajemen Rumah Sakit (SIMRS): Strategi Migrasi Monolith dan Evaluasi Performa Farizal Ginanjar; Rizqia Fauziah Rachma; Wahid Diyono; Mustawi Farhan; Windu Gata
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2040

Abstract

Digital transformation in Indonesia’s healthcare sector demands a Hospital Information System (SIMRS) that is scalable, reliable, and easy to integrate. Most existing SIMRS still rely on a monolithic architecture, making them vulnerable to single points of failure, hard to scale per module, and prone to downtime during updates. This study aims to examine strategies for migrating monolithic architectures to microservices and to evaluate the performance of a Docker- and Docker Swarm-based deployment on a SIMRS prototype consisting of twelve functional modules. The method combines a Systematic Literature Review on recent decomposition patterns with an experimental study on a two-node cluster running fourteen stacks and thirty-five services for a total of seventy-six active containers. Measurements show that the average HTTP response time ranges from 0.6 to 5.1 milliseconds for frontends (with a single billing-frontend outlier of 82.4 ms attributable to memory saturation) and from 0.8 to 3.0 milliseconds for backends, with per-container CPU utilization mostly below two percent and stable memory consumption within allocated limits. Docker Swarm proves effective in supporting high-availability replication, rolling updates, and automatic self-healing under normal operating conditions; one observed rolling-update failure (billing-backend dropping to 0/2 replicas until manual intervention) shows that reliable image-level health checks are a prerequisite for the promised zero-downtime behavior. The study concludes that microservices with Docker Swarm are a feasible adoption strategy for SIMRS in Indonesia, with manageable operational trade-offs.
Pengaruh Terpaan Konten Berita Negatif tentang Pemerintah di Instagram terhadap Persepsi Risiko Mahasiswa Universitas Paramadina Cikarang Zahwana Moza Aulia; Devi Atika Sari; Aris Subagio
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2053

Abstract

The increasing use of Instagram as a source of political information among university students, along with the widespread circulation of negative news content about the government, may influence how students perceive risks associated with government institutions. This study aims to examine and measure the effect of exposure to negative news content about the government on Instagram on the risk perception of students at Universitas Paramadina Kampus Cikarang. This study employed a quantitative approach with an explanatory research design. Data were collected through an online questionnaire survey involving 100 students selected using purposive sampling based on predetermined criteria. The data were analyzed using validity and reliability tests, assumption tests, and simple linear regression. The findings indicate that exposure to negative news content about the government on Instagram positive and significant effect on students' risk perception. This finding suggests that higher exposure to negative news content about the government is associated with higher levels of risk perception toward the government. Exposure to negative news content accounted for 52.8% of the variance in students' risk perception, while the remaining variance was attributable to other factors outside the research model. Therefore, exposure to negative news content on Instagram represents one of the factors contributing to the formation of students' risk perception toward the government.
Prediksi Diabetes Berdasarkan Hipertensi dan Obesitas Menggunakan Algoritma Logistic Regression di Puskesmas Setiabudi Intan Amelia Septiyani; Fuad Nur Hasan; Lestari Yusuf
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2073

Abstract

This study aimed to apply logistic regression to predict diabetes status among patients at Setiabudi Public Health Center and to analyze the relationship between predictor variables and the predicted probability of diabetes. The secondary dataset comprised 2,013 patients: 1,962 with a Normal status and 51 with a Diabetes status, yielding a class ratio of approximately 38:1. The model used age, gender, blood pressure, body mass index (BMI), waist circumference, and random blood glucose as predictors, with blood glucose status as the target. Model performance was evaluated using 10-fold cross-validation with out-of-fold (OOF) predictions, in which each patient received a prediction while their record was in a validation fold. The model achieved an accuracy of 99.95%, precision of 98.08%, recall of 100.00%, and an F1-score of 99.03%. These results should be interpreted in light of the class imbalance. Moreover, OOF evaluation provides internal validation on the study dataset rather than validation on independent or external test data. The results therefore do not establish how well the model would generalize to other populations or datasets.
Penerapan Metode Simple Additive Weighting (SAW) pada Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Berbasis Web Nurhasanah Nurhasanah; Nurhalimah Nurhalimah; Yuliana Yuliana; Kevin Novebrianto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2079

Abstract

Employees are vital assets for any institution, playing a crucial role in maintaining sustainability, driving development, and enhancing competitiveness. Optimal employee performance directly impacts organizational productivity, making periodic evaluations for selecting the best employee a necessity. However, this process often runs suboptimally due to the absence of a structured evaluation method. Human Resource Development (HRD) frequently faces difficulties caused by subjective assessment tendencies, which can trigger friction among employees. To address these issues, this study develops a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method. The evaluation criteria include responsibility, attendance, compliance with rules, teamwork, and work morale. The system testing was conducted using 12 employee records as simulated alternatives, where the calculation results demonstrated that this web-based system successfully produces a structured and precise evaluation, with the top rank achieved by the alternative securing a perfect preference score of 100.
Sistem Pakar Diagnosis Kanker Prostat Berbasis Web Menggunakan Logika Fuzzy Tsukamoto Munawaroh Munawaroh; Bangkit Abhimata Praja
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2081

Abstract

Prostate cancer is one of the malignancies most commonly affecting men, particularly older adults, and remains a major contributor to cancer morbidity and mortality worldwide. Diagnosing prostate cancer requires interpreting multiple clinical parameters that are often uncertain and gradual in nature, which complicates medical decision-making. This study aims to design and implement a web-based expert system for prostate cancer risk diagnosis using the Tsukamoto fuzzy logic method. The system was developed with the Laravel 10 framework, PHP 8.1, and a MySQL 8.0 database, using four clinical input variables: Prostate-Specific Antigen (PSA) level, patient age, International Prostate Symptom Score (IPSS), and PSA density. A knowledge base of 25 fuzzy rules was formulated through iterative interviews with urology specialists and validated against 50 electronic medical record cases from a referral hospital covering the 2021–2024 period. The inference process follows the Tsukamoto method through fuzzification, rule evaluation using the minimum operator, and weighted-average defuzzification, producing a risk percentage classified into low, medium, or high categories. Black-box testing of eight functional scenarios achieved a 100% success rate. Accuracy testing on 30 of these 50 cases, validated by a urology specialist, yielded an overall accuracy of 90%, with 27 of 30 cases correctly classified. These results indicate that the developed expert system can effectively support early screening of prostate cancer risk and serve as a decision-support tool for healthcare providers, particularly in facilities with limited access to urology specialists.
Perbandingan Algoritma Klasifikasi Decision Tree, Random Forest, dan Naive Bayes dalam Prediksi Kelulusan Mahasiswa Sartika Lina Mulani Sitio; Susanna Dwi Yulianti Kusuma; Hidayatullah Al Islami; Mukti Andrianto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2082

Abstract

Timely graduation is one of the important indicators of academic success of universities. However, the student monitoring process still faces obstacles in identifying students who have the potential to not graduate on time. This study offers a solution in the form of applying machine learning to predict graduation status while comparing the most effective algorithms. The research uses 750 data of students of the Informatics Study Program batch 2017 – 2021 sourced from the Academic Information System. The research methods include data preprocessing, handling missing values and outliers, encoding, normalization, forming Decision Tree, Random Forest, and Naïve Bayes models, as well as evaluation using accuracy, precision, recall, F1-score, and AUC-ROC. The results of the study show that Random Forest provides the best performance with an accuracy of 91.33%, precision of 90.48%, recall of 93.42%, F1-score of 91.93%, and an AUC-ROC of 0.952. Decision Tree obtained an accuracy of 84.67%, while Naïve Bayes was 80.00%. The Friedman test resulted in a value of p = 0.015 which showed a significant difference in performance. Thus, Random Forest is the best algorithm for predicting student graduation in this research dataset and has the potential to support academic early warning systems.
Robust Deep Learning Framework untuk Deteksi Cacat Permukaan pada Material Logam dalam Kondisi Pencitraan yang Terganggu Noise dan Blur Santi Rahayu; Achmad Hindasyah; Nur Annisahaq
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 3 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i3.2083

Abstract

Surface defect detection plays a crucial role in industrial quality control, especially in metal manufacturing, where defects can impact structural integrity and product reliability. Although deep learning-based inspection systems have shown promising results, most approaches assume ideal imaging conditions and experience significant performance degradation when faced with disturbances such as noise and blur. This study proposes a robust deep learning framework for surface defect detection under degraded imaging conditions. The framework integrates controlled image degradation simulation and a segmentation-based model using U-Net to enable precise pixel-level localization of defects. Robustness is evaluated through the addition of Gaussian noise, salt-and-pepper noise, and motion blur. Experimental results on the Severstal Steel Defect Dataset demonstrate that the proposed method outperforms the baseline segmentation model YOLOv8n-seg. Under clean conditions, the model achieves a precision of 0.6328 and an F1-score of 0.3199. In degraded conditions, the model remains stable despite performance drops, whereas other methods show significant declines. Additionally, the segmentation output allows for quantitative estimation of defect areas, providing valuable information for industrial applications. The findings indicate that segmentation-based approaches offer greater robustness and more reliable defect localization in challenging imaging environments.

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