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All Journal Jurnal Psikologi Islam sudo Jurnal Teknik Informatika Arcitech: Journal of Computer Science and Artificial Intelligence
Mauladi, Muhammad
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Religion Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Social Sciences

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Analisis Sentimen Masyarakat terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Support Vector Machine dan SMOTE Nurrahman, Adikara Alif; Mauladi, Muhammad; Rahman, Abdul
sudo Jurnal Teknik Informatika Vol. 4 No. 2 (2025): Edisi Juni
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v4i2.908

Abstract

Tujuan dari penelitian ini adalah untuk mengidentifikasi sentimen masyarakat terkait dengan kenaikan harga Bahan Bakar Minyak (BBM) menggunakan teknik Support Vector Machine (SVM). Data yang digunakan diambil dari komentar-komentar pada video YouTube mengenai topik ini, dengan total 462 komentar. Data tersebut kemudian melalui proses pembersihan, yang meliputi pengubahan huruf menjadi kecil, pemecahan teks menjadi token, penghapusan kata-kata tidak penting, dan pengembalian kata ke bentuk dasarnya. Mengingat adanya ketidakseimbangan antara sentimen positif dan negatif, teknik Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan kedua kelas sentimen. Hasil analisis menunjukkan bahwa model SVM berhasil mengklasifikasikan sentimen negatif dengan tingkat akurasi 94%, namun kurang efektif dalam mengidentifikasi sentimen positif yang hanya mencapai 38%. Setelah penerapan SMOTE, akurasi keseluruhan model meningkat menjadi 84%. Secara keseluruhan, penelitian ini menunjukkan bahwa SVM yang dipadukan dengan SMOTE dapat memberikan hasil yang memadai dalam menganalisis sentimen publik mengenai isu kenaikan harga BBM, meskipun sentimen negatif lebih dominan. Hasil ini juga menggarisbawahi pentingnya penyeimbangan data dalam meningkatkan efektivitas model klasifikasi.
Religiusitas penyandang tunanetra Mauladi, Muhammad; Reza, Iredho Fani; Kailani, Kailani
Jurnal Psikologi Islam Vol. 5 No. 2 (2018): Jurnal Psikologi Islam
Publisher : Asosiasi Psikologi Islam (API) Himpsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.991 KB) | DOI: 10.47399/jpi.v5i2.65

Abstract

People with blindness is a condition where individual experienced a weak visual impairment or invisible vision. Individuals with visual impairments tend to experience some holistic problems. This research is a type of narrative qualitative study. The subjects of this study were four people with visual impairments. The methods of data collection were used interview, observation, and documentation. The data were analyzed using Miles and Huberman techniques consists of data reduction, data display, and conclusion drawing / verification. The results indicated that religiosity in individuals with visual impairments revealed through a series of worship. This can make an acceptance with full of sincerity toward the life situations. This study also found two dimensions of religiosity in individuals with visual impairments through Islamic psychology perspective. First, hablum min Allah dimension who has behavioral indicators believing in the religion, prayers, zakat, fasting, know the pillars of Islam, harmonizes faith and has a better appreciation to Allah. Second, hablum min an-nas dimension who has prosocial behavior indicators, respect for older people, and advise one another in the kindness. Keywords: Religiosity, Sincerity, Blind People.
Klasifikasi Motif Kain Jumputan Palembang Menggunakan Metode CNN dengan Arsitektur Resnet-50 Mauladi, Muhammad; Hermanto, Dedy
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15310

Abstract

This study develops an automated classification system for Palembang jumputan textile motifs based on computer vision to address inter-motif pattern similarities that often challenge non-expert users and hinder the digital documentation of textile cultural heritage. Unlike traditional textile studies that typically employ generic Convolutional Neural Networks (CNNs), this research applies transfer learning using the ResNet-50 architecture on a primary dataset consisting of five motif classes: lilin, titik 7, titik 9, bunga tabur, and akoprin daun. The dataset is divided into training, validation, and testing sets, followed by preprocessing and image augmentation to enhance data variability. The model is trained with learning rate tuning, and the best configuration achieves a training accuracy of 95.57%, a validation accuracy of 87.33%, and a testing accuracy of 88%. Evaluation using a classification report and confusion matrix indicates excellent performance for the titik 9 and bunga tabur motifs, with precision and recall values approaching 1.00, while misclassifications still occur in the lilin motif due to visual similarity. These results confirm the effectiveness of ResNet-50 for jumputan motif classification and support cultural preservation through faster and more consistent motif identification.
Co-Authors Abdul Rahman Dedy Hermanto Iredho Fani Reza Kailani Kailani, Kailani Nurrahman, Adikara Alif