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Jurnal Minfo Polgan (JMP)
Published by Politeknik Ganesha Medan
ISSN : 20899424     EISSN : 27973298     DOI : -
Core Subject : Science, Education, Social,
Computer Science & IT Library & Information Science Mathematics Social Sciences
Jurnal Minfo Polgan (JMP) merupakan jurnal nasional yang diterbitkan oleh Program Studi Manajemen Informatika Politeknik Ganesha Medan terbit berkala (satu tahun dua kali yaitu Maret dan September) dengan tujuan untuk menyebarluaskan hasil riset bidang teknologi dan informasi kepada para akademisi, praktisi, mahasiswa, dan lain-lain. Jurnal Minfo Polgan (JMP) menerima kiriman artikel hasil riset bidang teknologi dan informasi yang ditulis dalam Bahasa Indonesia. Agar hasil riset bidang teknologi dan informasi yang dimuat dapat bermanfaat untuk pengembangan bidang teknologi dan informasi. Adapun ruang lingkup Jurnal Minfo Polgan adalah: 1. Sistem Pendukung Keputusan (SPK/DSS) 2. Sistem Informasi Geografis (GIS/SIG) 3. Sistem informasi skala enterprise (ERP, EAI, CRM, SCM) 4. Keamanan Sistem Informasi 5. Sistem Informasi Berbasis Web 6. Sistem Berbasis Pengetahuan & Data mining 7. Mobile Computing 8. Multimedia
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,058 Documents
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Analisis Pemanfaatan Aplikasi E-Ticketing Dengan Metode Technology Organization Environment (TOE) Framework Muhardono, Ari; Sunarjo, Wenti Ayu; Adriyana, Rika; Sari, Tarismha Winda; Ni'mah, Syarifatun
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15955

Abstract

The tourism industry is undergoing rapid transformation driven by digital technology adoption. E-ticketing systems have been widely implemented to improve operational efficiency and enhance visitor convenience. Tirta Arum Swimming Pool has adopted an e-ticketing application since 2024; however, its utilization remains suboptimal, as visitors continue to prefer on-site ticket purchases. This study investigates e-ticketing utilization from the management perspective using the Technology–Organization–Environment (TOE) framework. A qualitative case study approach was employed through in-depth interviews with ten managerial informants. Data were analyzed using thematic analysis supported by NVivo software, focusing on technological, organizational, and environmental dimensions. The findings indicate that the e-ticketing system is technologically reliable and user-friendly, contributing to improved operational efficiency, although further development is required in system integration and barcode validation. Organizational support and human resource readiness are strong, yet the absence of formal policies and standard operating procedures constrains optimal utilization. Environmentally, adequate infrastructure supports implementation, but limited promotion and entrenched visitor habits hinder the adoption of online ticketing features. Keywords: E-ticketing; TOE framework; tourism digitalization; qualitative study; management perspective
Perbandingan Algoritma Decision Tree dan Naive Bayes Pada Analisis Sentimen Masyarakat Terhadap Pejabat Pertamina Pasca Kasus Pertamax Oplosan Mubarak, Mubarak; Tanti, Lili; Rosnelly, Rika
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15971

Abstract

Penelitian ini membandingkan kinerja algoritma pembelajaran mesin, yaitu Decision Tree dan Naive Bayes, dalam menganalisis sentimen masyarakat terhadap pejabat Pertamina setelah kasus "Pertamax Oplosan". Analisis sentimen merupakan alat penting untuk memahami opini publik dan manajemen krisis reputasi. Kasus Pertamax Oplosan memicu kontroversi publik yang luas, dan analisis sentimen terhadap tanggapan pejabat Pertamina dapat memberikan wawasan mengenai efektivitas strategi komunikasi krisis perusahaan.Algoritma Decision Tree menawarkan model berbasis pohon keputusan yang intuitif dan mudah diinterpretasi, meskipun rentan terhadap overfitting. Sebaliknya, Naive Bayes, dengan pendekatan probabilistiknya, dikenal efisien secara komputasi, terutama pada dataset besar. Penelitian ini bertujuan untuk mengukur kinerja kedua algoritma dalam mengklasifikasikan sentimen (positif, negatif, atau netral) dari data teks yang dikumpulkan dari media sosial. Data yang digunakan terbatas pada data teks di Twitter dengan kata kunci "Pertamax Oplosan" dan difokuskan pada sentimen terhadap pejabat Pertamina, bukan perusahaan secara keseluruhan. Data mentah sebanyak 3928 komentar tweet berhasil dikumpulkan melalui API Twitter. Metodologi penelitian ini mencakup beberapa tahapan, yaitu pengambilan data (crawling), preprocessing data, pelabelan pola sentimen, ekstraksi fitur, pembagian dataset, klasifikasi, dan evaluasi model. Data dibagi menjadi data latih (training) dan data uji (testing) dengan kombinasi 80:20. Hasil evaluasi akan menggunakan matriks kebingungan (confusion matrix) untuk mengukur akurasi, presisi, recall, F1-score, dan ROC Analysis. Hasil penelitian ini diharapkan dapat memberikan rekomendasi algoritma yang paling sesuai untuk analisis sentimen serupa dan menjadi panduan praktis bagi perusahaan dalam mengelola krisis reputasi.
Web-Based Stroke Disease Classification System Using the Modified K-Nearest Neighbors Method Suwanda, Rizki; Bustami, Bustami; Khardawi, Muhammad
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16046

Abstract

Classification is a systematic method of grouping data based on predefined analytical rules and principles. One of the classification methods employed in this study is the Modified K-Nearest Neighbors (MKNN) algorithm, which is recognized for its potential to achieve higher accuracy. MKNN is an extension of the traditional K-Nearest Neighbors (KNN) method, incorporating an additional ranking stage and a weighted voting mechanism using an alpha value of 0.5. The object of this study is stroke disease. In the medical context, stroke occurs due to a disruption of blood flow to the brain. Ischemic stroke is caused by the obstruction of blood vessels and is generally considered less severe, whereas hemorrhagic stroke results from the rupture of blood vessels and is categorized as a severe condition. Hospitals in Indonesia are required to provide prompt and accurate healthcare services, in accordance with Law Number 36 of 2009 concerning Health. Approximately 70% of stroke patients have a history of hypertension and heart disease, while around 87% experience psychological disorders such as anxiety and depression. Based on data obtained from Cut Meutia Regional General Hospital (RSUD Cut Meutia) in Lhokseumawe, the classification of stroke types is still performed manually through clinical observation. Therefore, this study proposes a stroke classification system based on the MKNN algorithm. The system utilizes 11 features and two diagnostic classes, namely ischemic stroke and hemorrhagic stroke, with a total of 100 medical record datasets divided into 80 training data and 20 testing data. Using a value of K = 5, the system achieved an average confidence accuracy of 81.19%, with a precision of 85.71%, recall of 80%, F1-score of 82.75%, and overall accuracy of 75%. The system was developed using the PHP programming language and a MySQL database.
Pengaruh Struktur Modal dan Profitabilitas Terhadap Harga Saham Dengan Ukuran Perusahaan Sebagai Variabel Moderasi Michael Bungaran Sitanggang; Eli Safrida; Khanti Listya; Abdul Rahman
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16114

Abstract

This study aims to examine the effect of capital structure and profitability on stock prices, with firm size as a moderating variable, in energy sector companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. The background of this research is based on the inconsistency of previous study results regarding the influence of capital structure and profitability on stock prices, as well as the limited research on the moderating role of firm size, particularly in the energy sector. The theory used in this study is the Signaling Theory, which explains that capital structure and profitability can provide either positive or negative signals to investors, ultimately affecting the company's stock price. The sampling method employed in this research is purposive sampling, resulting in 82 samples drawn from 47 companies that constitute the research population. The method used in this study is multiple linear regression and Moderated Regression Analysis (MRA), with the assistance of SPSS version 29 for testing. The findings indicate that capital structure has a significant effect on stock prices, while profitability does not show a significant influence. Regarding the moderating role, firm size is proven to weaken the relationship between capital structure and stock prices but does not moderate the relationship between profitability and stock prices.
Predictive Modeling of Smartphone Addiction: Performance Evaluation of KNN, XGBoost, and Naive Bayes on Behavioral Patterns Wayahdi, M. Rhifky; Ruziq, Fahmi; Nasution, Auliana; Purwawijaya, Ellanda
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16178

Abstract

Excessive smartphone use has triggered a global crisis in the form of smartphone addiction, which negatively impacts mental health and productivity. Most current detection methods still rely on subjective questionnaires that are prone to bias. Therefore, this study aims to evaluate and compare the performance of machine learning-based predictive models—namely K-Nearest Neighbors (KNN), Naive Bayes, and Extreme Gradient Boosting (XGBoost)—in objectively classifying addiction levels based on user behavioral patterns. The research methodology adopts a standard machine learning workflow encompassing data preprocessing, model training, and performance evaluation using a dataset of 3,300 user activity log entries. Empirical results demonstrate that XGBoost yields superior classification performance, achieving an accuracy of 97.27% and an F1-Score of 96.70%, significantly outperforming the KNN (94.54%) and Naive Bayes (89.09%) algorithms. Further feature importance analysis confirms that App Usage Time is the most crucial predictor in detecting addiction. This study concludes that the XGBoost architecture is highly robust in handling non-linear behavioral feature interactions, enabling high-precision predictions. The implications of these findings provide a solid technical foundation for the development of automated early detection systems. Future research should consider expanding the dataset to include application categorization and integrating XGBoost modeling into real-time digital wellbeing application prototypes.
Analisis Sentimen Komentar Toxic pada Video Musik YouTube Menggunakan Metode Naive Bayes Romindo, Romindo; Sirait, Kevin Bastian; Riffan, Valentino; Wijaya, Chailine Garcia
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16190

Abstract

Platform berbagi video YouTube telah menjadi ruang interaksi digital yang masif, namun seiring pertumbuhannya, fenomena komentar toxic atau negatif semakin marak ditemukan pada konten-konten populer yang bersifat kontroversial. Penelitian ini bertujuan untuk menganalisis pola sentimen komentar toxic pada video musik "Hozier - Take Me to Church" serta mengukur performa algoritma Naive Bayes dalam melakukan klasifikasi sentimen secara otomatis. Data dikumpulkan menggunakan YouTube API dengan total 100.000 data komentar berbahasa Inggris, yang setelah melalui proses pembersihan menghasilkan 51.348 data valid. Proses text preprocessing mencakup lowercasing, penghapusan URL, tanda baca, angka, dan emotikon, diikuti tahap contraction, tokenization, penghapusan stopwords, serta lemmatization menggunakan library Natural Language Toolkit (NLTK). Data kemudian dilabeli ke dalam tiga kelas sentimen: positif, negatif, dan netral, sebelum diklasifikasi menggunakan algoritma Naive Bayes. Evaluasi performa model dilakukan dengan confusion matrix yang menghasilkan nilai akurasi sebesar 74%. Presisi untuk kelas negatif mencapai 96%, netral 97%, dan positif 64%. Nilai recall kelas negatif sebesar 57%, netral 49%, dan positif 99%. Sedangkan F1-score kelas negatif sebesar 71%, netral 65%, dan positif 78%. Dari total data yang diproses, ditemukan 8.475 komentar toxic (negatif), menunjukkan bahwa sebagian besar audiens merespons video tersebut secara positif. Hasil ini membuktikan bahwa algoritma Naive Bayes memiliki kemampuan yang memadai dalam klasifikasi sentimen komentar media sosial.
Pengaruh Citra Merek dan Ulasan Pelanggan Terhadap Keputusan Pembelian di Aplikasi Shopee: Studi Pada Mahasiswa Institut Teknologi Dan Bisnis Kristen Bukit Pengharaapan Agust Four, Philip Invokavid; Prakoso, Yusak
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16193

Abstract

“This study aims to analyze the influence of brand image and customer reviews on purchasing decisions on the Shopee platform. The study employs a quantitative approach. The research population consists of 280 students from the Institut Teknologi dan Bisnis Kristen Bukit Pengharapan, with a sample of 74 respondents determined using the Slovin formula. Data analysis is conducted using multiple linear regression. The results indicate that both brand image and customer reviews have a positive and significant effect on purchasing decisions. These findings suggest that brand perception and information derived from customer reviews are key factors in shaping consumer purchasing decisions in the e-commerce context.”
Pengembangan Sistem Pertahanan Server Cerdas Menggunakan Honeypot Dionaea dan Host-Based Intrusion Detection System (HIDS) dalam Deteksi Serangan Siber Tamrin, Fadli; Asrul, Asrul
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16224

Abstract

Meningkatnya intensitas dan kompleksitas serangan siber merupakan ancaman serius bagi keamanan server. Host-based Intrusion Detection System (HIDS) seperti Wazuh memiliki keterbatasan dalam mendeteksi beberapa jenis serangan, terutama pada lapisan aplikasi dan jaringan, sementara honeypot seperti Dionaea yang efektif menangkap serangan seringkali hanya digunakan untuk analisis pasif. Penelitian ini bertujuan untuk merancang, membangun, dan menguji sebuah sistem keamanan terintegrasi yang memanfaatkan honeypot Dionaea sebagai sensor aktif untuk meningkatkan kemampuan deteksi dan respons dari HIDS Wazuh Metodologi penelitian melibatkan pembangunan arsitektur dalam lingkungan virtual yang terdiri dari honeypot Dionaea, server Wazuh, agent yang dilindungi, dan mesin penyerang. Data serangan yang ditangkap oleh Dionaea diolah dan diteruskan ke Wazuh menggunakan agent. Aturan deteksi khusus (custom rules) dikembangkan pada Wazuh untuk menghasilkan peringatan, yang kemudian memicu mekanisme active response untuk memblokir alamat IP penyerang secara otomatis pada firewall di mesin yang dilindungi. Sistem diuji menggunakan berbagai skenario serangan, termasuk Port Scanning, Brute Force, dan Distributed Denial of Service (DDoS). Hasil pengujian menunjukkan bahwa integrasi sistem berhasil. Untuk serangan pada lapisan aplikasi seperti Brute Force dan Slowloris, sistem mampu mendeteksi, menghasilkan peringatan, dan secara otomatis memblokir IP penyerang, yang diverifikasi dengan kegagalan koneksi 100%. Untuk serangan lapisan jaringan seperti SYN Flood dan ICMP Flood, meskipun tidak tercatat oleh honeypot, dampaknya dapat terdeteksi sebagai anomali melalui lonjakan penggunaan sumber daya CPU pada host. Kesimpulannya, integrasi ini berhasil mengubah honeypot dari alat analisis pasif menjadi sensor pertahanan aktif, yang secara signifikan meningkatkan visibilitas dan kemampuan respons HIDS terhadap berbagai ancaman siber.

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