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All Journal Manajemen Bisnis Kompetensi International Journal of Artificial Intelligence Research Tropical Animal Science Journal Lentera: Jurnal Ilmiah Kependidikan Journal of Information Systems and Informatics JIPIS JIIP (Jurnal Ilmiah Ilmu Pendidikan) Asatiza: Jurnal Pendidikan Jurnal Sosial dan Humanis Sains Jurnal Pengabdian UMKM Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
M. Ardiansyah
Institute Pertanian Bogor, Kampus Baranangsiang, Bogor
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pengaruh Bentuk Tes Formatif Dan Tipe Kepribadian Terhadap Hasil Belajar Matematika Ardiansyah, M.; Nugraha, Mohamad Lutfi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 4, No 1 (2020): SEMNAS RISTEK 2020
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v4i1.2523

Abstract

Maksud dan Tujuan dari penelitian ini adalah untuk mengetahui perbedaan hasil belajar matematika peserta didik yang diberi tes formatif uraian terbatas dan bebas, dan mencari tahu perbedaan hasil belajar matematika peserta didik yang mempunyai tipe kepribadian ekstrovet dan introvert, serta mencari tahu apakah terdapat pengaruh interaksi hasil belajar matematika peserta didik yang diberi bentuk tes formatif uraian terbatas dan uraian bebas dengan tipe kepribadian peserta didik. Tempat penelitian ini dilaksanakan di SMA Uswatun Hasanah dengan metode penelitian yang dipakai adalah eksperimen.  Sebanyak 48 peserta didik yang dijadikan sampel penelitian, diantaranya 12 siswa kelas kontrol dan 12 siswa kelas eksperimen. Data dikumpulkan dengan cara menyebar angket langsung kepada sampel. Analisis data menggunakan statistika deskriptif seperti mencari mean, median, modus, standar deviasi, dan Analisis inferensial yaitu analisis varian va dua jalur. Hasil penelitian ini adalah Terdapat perbedaan hasil belajar matematika peserta didik  yang diberi bentuk tes formatif uraian terbatas lebih tinggi dari pada uraian bebas. Jadi terdapat pengaruh yang sangat signifikan bentuk tes formatif uraian terbatas terhadap hasil belajar matematika peserta didik. Terdapat perbedaan hasil belajar matematika peserta didik  yang bertipe kepribadian ekstrovert lebih tinggi dari yang bertipe kepribadian introvert. Jadi terdapat pengaruh yang sangat signifikan tipe kepribadian ekstrovert terhadap hasil belajar matematika peserta didik, Terdapat pengaruh interaksi hasil belajar matematika  peserta didik yang diberi bentuk tes formatif uraian terbatas dan uraian bebas dengan tipe kepribadian peserta didik.
Chemical and Physical Quality, Fermentation Characteristics, Aerobic Stability, and Ruminal Degradability of Sorghum Silage Inoculated with Lactiplantibacillus plantarum and Limosilactobacillus fermentum Fitriani, D.; Ardiansyah, M.; Kurniawati, A.; Bachruddin, Z.; Paradhipta, D. H. V.
Tropical Animal Science Journal Vol. 47 No. 4 (2024): Tropical Animal Science Journal
Publisher : Faculty of Animal Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5398/tasj.2024.47.4.483

Abstract

This study was carried out to determine the effect of homo (Lactiplantibacillus plantarum FNCC 0020) and hetero (Limosilactobacillus fermentum BN21) fermentative lactic acid bacteria on chemical compositions, fermentation characteristics, aerobic stability, and ruminal digestibility of sorghum (Sorghum bicolor L. Moench) silage. The sorghum forage was harvested at the milk ripening phase with a dry matter content of 25.6% and fermented for 100 days with different inoculants: treatments without inoculant (CON), L. plantarum (LP), L. fermentum (LF) as well as a mixture of LP and LF at a ratio of 1:1 (MIX). The experiment was conducted using a completely randomized design with 5 replications per treatment, and all inoculants were applied at 105 cfu/g of fresh forage. The results showed that LF silage caused a 66.3% reduction in cyanide acid content, the lowest mold count, and longer aerobic stability compared to LP and CON. The lowest pH (p<0.05) and highest organic matter digestibility (p<0.05) were obtained on LP silage, while the CON silage showed no significant difference. The LP and LF silage showed the highest total volatile fatty acid (p<0.05), while there was no significant between CON and others. The LF silage had the highest acetate and the lowest propionate (p<0.05). These results showed that L. fermentum was more effective in decreasing cyanide acid content and increasing the aerobic stability of sorghum silage, while L. plantarum was able to lower pH and reduce ammonia concentration.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Forensic Analysis of AI-Generated Image Alterations Using Metadata Evaluation, ELA, and Noise Pattern Analysis Ferdiansyah, Ferdiansyah; Deazwara, Muhammad Rizki Akbar; Billanivo, Reynaldi Rizki; Ardiansyah, M.; Ilham, Ilham
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1362

Abstract

This study develops a forensic workflow to assess the authenticity of digital images, addressing the challenge of distinguishing AI-generated content from real photographs. The goal is to analyze metadata, compression behavior, and noise characteristics to identify synthetic images. The dataset includes eight images: two original Xiaomi 14T Pro photos and six AI-generated variants from Gemini, ChatGPT, and Copilot. Metadata was extracted using ExifTool version 13.25 on Kali Linux, while Error Level Analysis (ELA) and Noise Pattern Analysis (NPA) were performed with consistent parameters on the Forensically platform. Authentic images displayed complete EXIF metadata, uniform compression patterns, and stochastic sensor noise. In contrast, AI-generated images lacked EXIF data, included XMP or C2PA provenance, exhibited localized compression anomalies, and showed smoother, more structured noise patterns. The study presents a practical and reproducible forensic workflow that integrates metadata evaluation, ELA, and noise analysis to detect synthetic content. The findings demonstrate that despite their visual realism, AI-generated images still leave detectable forensic traces, offering valuable tools for image authenticity verification.
Co-Authors A. Kurniawati Ade Silvia Handayani Afifah, Luthfia Agung, Muhammad Zakuan Anggalana Asriyadi Asriyadi Billanivo, Reynaldi Rizki C. T. Noviandi Chaniago, Wahyu Ramadhan Deazwara, Muhammad Rizki Akbar Emilia Hesti Erlina B Farid, Ahmad Salman Ferdiansyah Ferdiansyah Fitriani, D. Hafsari, Rizka Handayani, Agustuti Hanindyalaila Pienrasmi Hartabela, Dadang Hudaya, Barda Ilham Ilham Kaila, Afifah Syifah Leon, Farah Margaretha Lukmanul Hakim Lukmanul Hakim Marudut Jon Ferry Simarmata Nugraha, Mohamad Lutfi Paradhipta, D. H. V. Rosita, Ella Sari, Rani Aprilia Suharto, Tentiyo suzan zefi Z. Bachruddin