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All Journal Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA JSI (Jurnal sistem Informasi) Universitas Suryadarma JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research Jurnal Manajemen Informatika Jayakarta JURNAL PENDIDIKAN DAN ILMU FISIKA
Hendarman Lubis
Universitas Bhayangkara Jakarta Raya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Meningkatkan Pemikiran Kritis dalam Pendidikan Fisika melalui AI: Tinjauan Literatur Sistematis tentang Tren dan Implikasi Pedagogis Hendarman Lubis; Vina N. Van Harling; Victor Bintang Panunggul
Jurnal Pendidikan dan Ilmu Fisika Vol 5 No 2 (2025): Desember 2025
Publisher : Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jpif.v5i2.43353

Abstract

This study aims to map research trends and analyze the pedagogical role of Artificial Intelligence (AI) in enhancing students' critical thinking skills in physics education. Employing a Systematic Literature Review (SLR) method guided by PRISMA protocols and enriched with bibliometric analysis, this study reviewed Scopus-indexed articles published between 2015 and 2025. Based on 42 mapped articles and an in-depth analysis of 12 key studies, the results indicate a significant trend shift post-2023 toward the use of Generative AI. Key findings reveal that "Role Reversal" pedagogical strategies and the use of "Socratic Tutors" are the most effective approaches for stimulating logical reasoning and scientific argumentation. In conclusion, AI has transformed from a mere visualization tool into a cognitive partner, despite remaining limitations in spatial reasoning. The study’s implications recommend the necessity of redesigning physics assessments to focus on critical validation of technological outputs rather than final answers, alongside strengthening ethical literacy to prevent student cognitive dependency.
IDENTIFIKASI KEMATANGAN BUAH ALPUKAT (AVOCADO) MENGGUNAKAN ALGORITMA ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Nurfiyah Nurfiyah; Hendarman Lubis; Ratna Salkiawati
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2473

Abstract

Avocado (Persea americana) is a horticultural commodity with high economic value but is climacteric, so the ripening process occurs quickly after harvest. Determining the ripeness level is still done conventionally (visually and manually) often results in subjective assessments and damages the fruit. This study aims to develop a non-destructive avocado ripeness identification system (unripe, ripe, and overripe) using the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm. The input parameters used are based on the Red-Green-Blue (RGB) color features and Gray Level Co-occurrence Matrix (GLCM) texture features extracted from digital images of avocados. The test results show that the combination of the ANFIS network architecture with a Gaussian membership function is able to recognize the ripeness level of avocados with an accuracy of up to 93.3% on the test data. This system is expected to be a technological solution for farmers and distributors in the process of sorting fruit objectively and quickly.
OPTIMALISASI SISTEM REKOMENDASI PENYEWAAN TRUK MENGGUNAKAN CONTENT BASED FILTERING Aida Fitriyani; Rakhmi Khalida; Hendarman Lubis
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2452

Abstract

In general, people do not have information in choosing the type of truck based on the load requirements and the type of goods to be transported when renting a truck. The high risk of accidents usually occurs due to overcapacity, vehicle mismatch with needs. Precisely determining the type of logistics fleet can now be accommodated through a web-based recommendation system equipped with a direct booking module. Through this platform, users can specify vehicles specifically based on load parameters, such as weight capacity and commodity category. In its development, the Waterfall methodology was systematically applied, encompassing the stages of needs analysis, system design, program code writing, testing, and periodic maintenance. The accuracy of this system was evaluated using the Cosine Similarity algorithm . Based on the trial results, the system successfully produced a very high level of recommendation similarity, with a coefficient value reaching 0.9487. The implementation of this technology has proven to be able to optimize the accuracy of fleet selection, reduce rental process time, and minimize security risks during the logistics distribution process
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.
Co-Authors Abrar Hiswara Achmad Noema Achmad Noeman Agus Hidayat Aida Fitriyani Alexander, Allan D Andy Achmad Hendharsetiawan Arif Rifai Dwiyanto Asep Ramdhani Mahbub Dani Yusuf Dwi Budi Srisulistiowati Dwipa Handayani Hantoro, Kusdarnowo Joniwarta M. Hadi Prayitno Muhammad Yasir Nurfiyah Rakhmi Khalida Rasim Rasim Ratna Salkiawati Ridwan Ridwan Robertus Suraji Uus Rusmawan Victor Bintang Panunggul Vina N. Van Harling