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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA International Journal of Management and Business Economics Jurnal Teknologi Informasi International Journal of Applied Information Systems and Informatics Manexia
Gendhi Haris
Universitas Teuku Umar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Implementasi Data Mining Tingkat Kepemimpinan Siswa dengan K-Nearest Neighbor, Decision Tree, dan Naïve Bayes Didin Sayhidin; Gendhi Haris; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5351

Abstract

The process of monitoring and evaluating high school student leadership is deemed necessary because the level of student leadership is one of the prerequisites for high school students to face real challenges in the future. Data mining can be used to classify the level of leadership among high school students. The purpose of the research conducted in this case is to apply data mining using the K-NN, Decision Trees, and Naive Bayes models. This research is located in two different public high schools, namely SMA A as training data and SMA B as test data. This data was obtained in the same year, namely 2022. The data obtained were analyzed with the help of the Rapidminer application using K-NN, Decision Tree, and Naive Bayes. Student data that is processed is Basic Education Data (DAPODIK) in excel format. Before being analyzed, the text is processed first, namely tokenization, case folding, stop words, and details. The main goal of the steps above is also the main goal of this study to get the most accurate algorithm for classifying student leadership levels and knowing the results for comparison. The conclusion of this study is when measuring the performance of the three algorithms, the test results use confusion matrix validation. The K-NN algorithm was found to have the highest accuracy score compared to the Decision Tree and Naive Bayes. The accuracy value of the K-NN method using a dataset of high school students is 95.86%, the accuracy value of the Decision Tree algorithm is 94.65%, and the accuracy value of the Naïve Bayes algorithm is 79.55%.
Analysis of the Usability Level of the JKN Mobile Application Using the User Experience Questionnaire (UEQ) and Importance–Performance Analysis (IPA) Methods Haris, Gendhi; Tri Julianto, Indri; Ridwan Ibrahim, Maulana
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17008

Abstract

The JKN Mobile application developed by BPJS Kesehatan is a digital service for National Health Insurance (JKN) participants to access features for checking membership, online queues, and complaint services. JKN Mobile contributes significantly to the digitalization of healthcare services, but various negative reviews still emerge, especially regarding the interface aspect. This study analyzes the usability level of the application using the User Experience Questionnaire (UEQ) and Importance Performance Analysis (IPA) methods. The UEQ assesses six aspects of user experience, while IPA maps attributes based on importance and performance. The analysis results show a gap between expectations and experience. Clarity (1.1056, 0.4021), efficiency (1.0181, 0.0026), and appeal (1.0946, -0.019) fall into the "maintain performance" quadrant. Novelty (0.0965, 0.4113) is in the "top priority" quadrant, stimulation (0.8763, -0.1192) is a low priority, while accuracy (0.9514, -0.1224) is excessive. These findings provide a comprehensive overview of aspects that need to be maintained or improved. User feedback-driven strategies and agile approaches are recommended to make application development more innovative, optimal, and responsive to the needs of digital healthcare services.
Analisis Technology Acceptance Model (Tam) Terhadap Tingkat Penerimaan Aplikasi PLN Mobile Pada PT. PLN (Persero) UP3 Meulaboh ardiansyah, muhammad; Aidina, Fitri Riza; sanusi, sanusi; Haris, Gendhi; Astrianda, Nica; Usman, Muhammad
Jurnal Teknologi Informasi Vol 4, No 2 (2025): Oktober
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jti.v4i2.11342

Abstract

Listrik merupakan sumber daya energi siap pakai yang tidak terlepas dari Teknologi Informasi (TI) Aplikasi PLN Mobile adalah aplikasi layanan PLN. Melalui aplikasi PLN Mobile, pelanggan banyak mendapatkan kemudahan diantaranya mengetahui berbagai info mulai dari transaksi token, lokasi pembayaran melalui banking terdekat, tagihan rekening listrik dan riwayat pemakaian Kwh listrik, layanan pengaduan dan keluhan dari pelanggan. Tujuan yang ingin dicapai dari penelitian ini adalah untuk mengetahui tingkat penerimaan pengguna menggunakan Aplikasi PLN Mobile dengan metode Technology Acceptance Model (TAM), untuk mengetahui pengaruh persepsi kemudahan dan keguanaan terhadap penerimaan pengguna aplikasi PLN Mobile pengguna sistem informasi aplikasi PLN Mobile pada pelanggan PT. PLN. (Persero) UP3 Meulaboh. Berdasarkan  hasil penelitian menunjukan bahwa secara determinasi diperoleh hasil penerimaan aplikasi PLN Mobile terhadap kemudahan dan kegunaan sistem informasi aplikasi PLN Mobile dengan metode TAM pada PT. PLN (Persero) UP3 Meulaboh termasuk katagori sangat setuju pada manfaat yang dihasilkan oleh sistem informasi aplikasi PLN Mobile sebesar 98,50%. Sedangkan 1,50% berada pada kategori tidak setuju.  Secara kuantitatif variabel  kemudahan aplikasi PLN Mobile dengan metode TAM pada PT. PLN (Persero) UP3 Meulaboh (Perceived Easy Of Use) secara individu sangat berpengaruh signifikan terhadap penerimaan pengguna sistem informasi aplikasi PLN Mobile. Besarnya pengaruh untuk variabel kemudahan dan kegunaan terhadap penerimaan pengguna sistem informasi aplikasi PLN Mobile hanya sebesar 0,965 atau 96,50%. Sedangkan hubungan antara keduanya sebesar 0,982  atau 98,20%. Jadi nilai ini menunjukan bahwa kemudahan sistem informasi aplikasi PLN Mobile mempunyai hubungan yang sangat tinggi terhadap penerimaan pengguna sistem informasi aplikasi PLN Mobile. 
From Content Automation to Capability Trap: Rethinking Marketing Capabilities in the Era of Generative AI Gendhi Haris
Manexia: Journal of Business, Management, and Creative Economy Vol. 1 No. 2 (2025): Strategic Reconfiguration and Generative AI in Marketing and Creative Economy
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.01203

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) has intensified claims that AI systematically enhances marketing capabilities. While prior research emphasizes efficiency gains, personalization, and agility, it largely assumes a linear relationship between AI integration and strategic performance. This article challenges that assumption by developing a conceptual framework of the Generative AI Capability Trap. Drawing on dynamic capabilities theory, competence trap logic, and emerging research on digital authenticity, the relationship between GenAI intensity and marketing distinctiveness is theorized as inherently non-linear. Moderate AI integration may amplify sensing and seizing capabilities, expand creative throughput, and improve short-term performance. However, excessive reliance may compress symbolic variance, reinforce exploitation bias through KPI-driven optimization, and gradually erode adaptive creative capacity. This erosion dynamic is driven by three mechanisms—pattern convergence, creative deskilling, and algorithmic reinforcement—and is conditioned by boundary factors related to organizational learning orientation, creative governance, and industry dynamism. By reframing generative AI as a dual-edged strategic infrastructure, this study extends capability theory to probabilistic systems and introduces variance preservation as a critical lens for evaluating AI-enabled marketing transformation.
Social Media Influencers and Consumer Purchase Intention: A Systematic Review Of Antecedents, Mediators, and Moderators Muhammad Rezaldi; Daniel Bonartua Malau; Gendhi Haris; Nova Novitasari
International Journal of Management and Business Economics Vol. 4 No. 3 (2026): June
Publisher : CV Putra Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58540/ijmebe.v4i3.1794

Abstract

This study conducts a focused systematic literature review of 15 empirically rigorous studies published between 2020 and 2025, selected from an initial pool of 65 candidate articles identified through a Scopus database search. Selection prioritized studies that explicitly reported effect sizes, employed structural equation modeling or experimental design, and examined purchase intention as a primary outcome variable. Guided by three research questions, RQ1: what influencer attributes are empirically associated with consumer purchase intention and with what relative strength? RQ2: what psychological mechanisms mediate the relationship between influencer attributes and purchase intention? RQ3: what contextual factors moderate the effectiveness of social media influencers on purchase intention? The review applies the Stimulus-Organism-Response (S-O-R) framework as a unifying theoretical architecture. Trustworthiness emerges as the most consistent predictor (RQ1), information credibility, utilitarian value, and parasocial relationships mediate effects (RQ2), and product category, generational cohort, and influencer type moderate outcomes (RQ3). Research gaps include cross-cultural comparisons, longitudinal designs, and over-endorsement saturation thresholds.
Co-Authors Aidina, Fitri Riza Christina Juliane Daniel Bonartua Malau Didin Sayhidin Indri Tri Julianto Muhammad Ardiansyah Muhammad Rezaldi Muhammad Usman, Muhammad Nica Astrianda Nova Novitasari Ridwan Ibrahim, Maulana Sanusi Sanusi