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All Journal JURIKOM (Jurnal Riset Komputer) Majalah Ilmiah Warta Dharmawangsa Jurnal Abdi Insani Indonesian Journal of Innovation Studies Academia Open
Tinambunan, Medi Hermanto
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ANALISIS METODE HOLT-WINTERS EXPONENTIAL SMOOTHING DALAM PREDIKSI EKSPOR KOMODITAS UTAMA 3 DIJIT SITC Tinambunan, Medi Hermanto; Wahyuni, Sri
Jurnal Warta Dharmawangsa Vol 18, No 1 (2024)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/wdw.v18i1.4256

Abstract

PELATIHAN TPACK 4C UNTUK PENINGKATKAN PEMBELAJARAN ABAD 21 DI SMK N 3 TONDANO Mahendra, I Gede Budi; Tinambunan, Medi Hermanto; Kembuan, Djubir R.E.
Jurnal Abdi Insani Vol 12 No 9 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i9.3024

Abstract

This community service activity was carried out at SMK Negeri 3 Tondano with the aim of improving teacher competency in designing and implementing TPACK-based learning and 21st-century skills through the 4C approach, namely communication, collaboration, critical thinking, and creativity. The implementation methods included socialization, training, lesson plan development workshops, microteaching, and classroom implementation. Evaluation was carried out through pre-tests and post-tests, observations, and interviews with teachers and students. The training results showed a significant increase in teacher understanding, with the average score increasing from 62.3 to 83.7 with an N-Gain of 0.58. In more detail, TPACK understanding increased from 61.2 to 83.5, 4C implementation from 63.4 to 85.2, HOTS integration from 60.1 to 81.7, and ICT utilization from 64.6 to 86.4. At the microteaching stage, 70% of teachers were able to communicate effectively, 65% facilitated collaborative discussions, and 55% used digital media well. Classroom implementation also had a positive impact on students, with 85 percent finding learning more engaging and 78 percent more motivated. Thus, this training has been proven to improve teacher professionalism while creating a more interactive, collaborative, and relevant learning experience for the needs of the 21st century. This activity recommends continued mentoring, the establishment of a forum for sharing good practices, revisions to suboptimal lesson plans, and digital infrastructure support to strengthen the ongoing implementation of TPACK and 4C.
Web-Based Priority Program Recommendation Using Collaborative Filtering: Rekomendasi Program Prioritas Berbasis Web Menggunakan Collaborative Filtering Pesik, Luisa Maria; Tinambunan, Medi Hermanto; Santa, Kristofel
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1882

Abstract

General Background: Recess activities of regional legislative members function as a formal mechanism for collecting diverse public aspirations related to regional development programs. Specific Background: In Minahasa Regency, the determination of priority programs derived from recess activities has traditionally relied on manual procedures that are prone to subjectivity and inconsistency. Knowledge Gap: Despite extensive studies on collaborative filtering in various domains, its application for managing and prioritizing legislative recess programs within local government contexts remains limited. Aims: This study aims to design and implement a web-based priority program recommendation system for DPRD recess activities using an Item-Based Collaborative Filtering algorithm. Results: The system was developed using the Waterfall method and implemented with PHP and MySQL, incorporating modules for aspiration management, rating, authentication, and recommendation generation. Black box testing across core functionalities confirmed that the system operated according to specifications and supported structured priority ranking of programs. Novelty: The research introduces the application of Item-Based Collaborative Filtering to DPRD recess aspiration management, replacing a previously manual and subjective process. Implications: The proposed system provides a structured, transparent, and data-driven approach to supporting legislative decision-making in prioritizing public programs at the regional level. Highlights• The developed system organizes recess aspirations into measurable program priorities• Item-based recommendation logic supports consistent ranking of proposed programs• Web-based architecture enables structured management of legislative aspiration data KeywordsCollaborative Filtering; Recommendation System; DPRD Recess; Priority Program; Web-Based Application
Aplikasi Monitoring Aset Berbasis QR Code Dengan Rule-Based Alert Engine Sebagai Peringatan Dini Sibarani, Gitarosalina; Santa, Kristofel; Tinambunan, Medi Hermanto
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9561

Abstract

Asset management at PT PLN Nusantara Power Unit Pembangkitan Minahasa is still carried out manually, which has the potential to cause recording errors and delays in data updates. In addition, most QR Code-based asset management systems that have been developed generally only focus on the process of recording and identifying assets, without being equipped with an early warning mechanism. This study aims to develop a web-based asset monitoring application that integrates QR Code technology with a rule-based alert engine using the Extreme Programming (XP) method. The research methods include iterative software development, functional testing using black box testing, system response time analysis, and user satisfaction evaluation through questionnaires. Testing results show that all system functions run according to user requirements with an average response time of less than 5 seconds, thus supporting real-time use. The rule-based alert engine is capable of automatically detecting asset conditions that require attention and generating notifications as a form of early warning. User evaluation shows a high level of satisfaction with the ease of use and functionality of the system. Based on these results, the developed application has been proven to improve the accuracy of recording and efficiency of asset management compared to manual methods, and has the potential to become an adaptive digital solution in office asset management.
Danantara YouTube Sentiment Shows Public Transparency Concerns: Sentimen YouTube Danantara Menunjukkan Kekhawatiran Transparansi Publik Wahani, Waraney Vincent Beckham; Hasibuan, Alfiansyah; Tinambunan, Medi Hermanto
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.14210

Abstract

General Background Social media comments offer valuable data for analyzing public discourse on policy issues. Specific Background This study investigates YouTube comments about Danantara, Indonesia's strategic investment body, using Natural Language Processing with 7,294 comments. Knowledge Gap Previous studies often analyze sentiment and topics separately, without integrated analysis or iterative labeling. Aims The study aims to classify sentiment using Support Vector Machine (SVM) and identify topics with Latent Dirichlet Allocation (LDA). Results 74.9% accuracy was achieved with SVM, classifying 58.0% of comments as negative, 29.3% neutral, and 12.8% positive. LDA revealed 6 topics for neutral, 4 for positive, and 3 for negative sentiment, with key concerns about transparency and corruption. Novelty This study integrates SVM and LDA with Human in the Loop labeling to capture both sentiment and topic substance. Implications Findings offer insights for improving transparency and public communication, while contributing to text mining in digital discourse. Highlights • The classifier achieved 74.9% accuracy after Human in the Loop labeling and manual verification.• Unfavorable polarity reached 58.0%, followed by neutral at 29.3% and positive at 12.8%.• Coherence scores selected 6 neutral, 4 positive, and 3 critical thematic clusters. Keywords Danantara; Sentiment Analysis; Topic Modeling; Support Vector Machine; Latent Dirichlet Allocation  
Co-Authors Hasibuan, Alfiansyah I Gede Budi Mahendra Kembuan, Djubir R.E. Kristofel Santa Pesik, Luisa Maria Sibarani, Gitarosalina Sri Wahyuni Wahani, Waraney Vincent Beckham