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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING IKRA-ITH EKONOMIKA Jurnal Teknik Mesin
Rahman Saputra, Rahman
Unknown Affiliation
Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Perancangan Mesin Perontok Padi Dengan Sistem Rotari Pristiansyah, Pristiansyah; Hasdiansah, Hasdiansah; Rohman, Habibu; Saputra, Rahman; Aprilia, Silvy
Jurnal Teknik Mesin Vol 18 No 1 (2025): Jurnal Teknik Mesin
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jtm.18.1.1766

Abstract

Rice is one of the staple foods consumed by the people of Indonesia, including the Bangka Belitung Islands Province, more precisely in Bangka Regency, Riau Silip District, Banyu Asin Village. Newly harvested rice is usually not yet separated from the straw/panicle. The rice harvesting process still uses traditional tools or also called gebotan. One example is the rice threshing machine in Banyu Asin Village which has not been able to maximize the threshing results, which results in a long threshing process. The machine is only capable of threshing 30 kg/hour of rice that is put into the machine. Based on these needs, a rice threshing machine design was made to facilitate the threshing process. The design of the rice threshing machine refers to the design method, namely: planning, conceptualizing, designing, and finishing, then assessed based on technical and economic aspects. The designed machine uses a rotary system to thresh rice from its stalks, uses a combustion engine as a driving system, pulleys and belts as transmission elements, and the machine can be moved because it is lighter than existing machines. From the design results, this rice threshing machine has a minimum capacity of 80 kg/hour.
Efektivitas Pembiayaan Syariah Kelompok Dalam Meningkatan Pendapatan Pelaku Umkm Ditinjau Dari Perspektif Etika Bisnis Islam (Studi Pada Nasabah Bank BTPN Di Kabupaten Bone) Saputra, Rahman; Novianty, Rina; Idayanti, Rini
IKRAITH-EKONOMIKA Vol. 8 No. 2 (2025): IKRAITH-EKONOMIKA Vol 8 No 2 Juli 2025
Publisher : Universitas Persada Indonesia YAI

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Abstract

Penelitian ini mengkaji efektivitas pembiayaan syariah kelompok dalam meningkatkanpendapatan pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) di Kabupaten Bone, ditinjau dariperspektif etika bisnis Islam. Dilatarbelakangi oleh keterbatasan modal yang dihadapi UMKM danperan penting Bank BTPN Syariah dalam pemberdayaan ekonomi berbasis syariah, penelitian inibertujuan untuk menganalisis sejauh mana pembiayaan tersebut efektif dan bagaimana prinsipprinsipetika bisnis Islam diterapkan. Menggunakan pendekatan kualitatif dengan wawancaramendalam terhadap nasabah UMKM, pegawai bank, dan akademisi, serta didukung dokumentasi,hasil penelitian menunjukkan bahwa pembiayaan syariah kelompok BTPN Syariah sangat efektif.Hal ini terlihat dari kemudahan akses dan proses pengajuan tanpa agunan, pemanfaatan dana yangproduktif untuk peningkatan stok dan kapasitas produksi, serta dampak signifikan terhadappeningkatan pendapatan UMKM (30-100%). Keberlanjutan usaha juga terjaga berkat sistemcicilan yang ringan dan solusi fleksibel dari bank saat nasabah menghadapi kendala. Lebih lanjut,penelitian ini menemukan bahwa prinsip-prinsip etika bisnis Islam, seperti kejujuran, transparansi,keadilan, tanggung jawab sosial, amanah, dan profesionalisme, telah diterapkan secara konsistendalam setiap aspek interaksi dan operasional pembiayaan. Penerapan nilai-nilai ini tidak hanyamembangun kepercayaan nasabah tetapi juga mendorong pemberdayaan ekonomi yang holistikdan berkelanjutan, sejalan dengan tujuan syariah
Generative AI Image Sentiment Analysis on Social Media X using TF-IDF and FastText Saputra, Rahman; Pristyanto, Yoga; Fajri, Ika Nur
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10627

Abstract

This research investigates public opinion on AI-generated images on Social Media X using machine learning-driven text classification. Three classification models were evaluated: Complement Naïve Bayes (CNB) utilizing TF-IDF features, Support Vector Machine (SVM) merging TF-IDF with FastText embeddings, and IndoBERT as a modern transformer-based baseline. A total of 1,958 Indonesian tweets were collected via web scraping with relevant keywords, followed by a pipeline involving text cleaning, manual labeling into positive, negative, and neutral categories, and data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) for the classical models (with class weighting applied for IndoBERT). Results show that the SVM model outperformed the others, achieving 68.7% accuracy with average precision, recall, and F1-score of 0.69, 0.69, and 0.68, respectively; CNB attained 64.1% accuracy with average metrics of 0.64; while IndoBERT recorded 58.2% accuracy with average precision, recall, and F1-score of 0.58, 0.58, and 0.57. Confusion matrix analysis revealed SVM's superior ability to distinguish positive and neutral sentiments in casual language, though IndoBERT demonstrated potential for capturing deeper semantic nuances despite underperforming due to dataset size and informal text. The findings highlight the efficacy of integrating statistical and semantic representations for improved sentiment analysis on unstructured, noisy social media data related to AI-generated imagery, while suggesting that transformer models like IndoBERT may benefit from larger datasets for optimal performance.
Co-Authors Aprilia, Silvy Fajri, Ika Nur Hasdiansah, Hasdiansah Idayanti, Rini Pristiansyah, Pristiansyah Rina Novianty, Rina Rohman, Habibu Yoga Pristyanto