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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Ilmiah Ecosystem Jurnal Sistem Informasi Kaputama (JSIK) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Scientific Journal of Informatics PUBLIDIMAS CSRID Access: Journal of Accounting, Finance and Sharia Accounting
Setiawan, Adil
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Analisis Transparansi Dan Akuntabilitas Pengelolaan Dana Bantuan Operasional Sekolah di SMAN 12 Makassar Kartika, Suci; Mane, Arifuddin; Setiawan, Adil
ACCESS: Journal of Accounting, Finance and Sharia Accounting Vol. 1 No. 3 (2023): ACCESS: Journal of Accounting, Finace and Sharia Accounting, Desember 2023
Publisher : Program Studi Akuntasi Universitas Bosowa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56326/access.v1i3.2046

Abstract

Tujuan penelitian untuk mengetahui transparansi dan kkuntabilitas Pengelolaan Dana Bantuan Operasional Sekolah (BOS) di SMAN 12 Makassar. Penelitian menggunakan pemeriksaan strategis deskriptif kualitatif, informasi yang dimanfaatkan adalah mengumpulkan informasi dari objek eksplorasi dan penyelidikan Transparansi dan Akuntabilitas Pengelolaan Dana Bantuan Operasional Sekolah (BOS). Strategi pengumpulan informasi dalam review, khususnya melalui pertemuan dan pencatatan, telah diperoleh informasi selama 3 tahun sebelumnya. Kedalaman dari peninjauan tersebut menunjukkan bahwa SMAN 12 Makassar, telah melaksanakan Transparansi dan Akuntabilitas Pengelolaan Dana Bantuan Operasional Sekolah (BOS) secara baik dan berkembang secara konsisten. Hal ini harus terlihat dari Laporan Pertanggungjawaban Dana Bantuan Operasional Sekolah (BOS) sesuai pengaturan dan Arahan khusus Dana BOS. The aim of the research is to determine the transparency and accountability of Management of School Operational Assistance Funds (BOS) at SMAN 12 Makassar. The research uses qualitative descriptive strategic examination, the information used is collecting information from objects of exploration and investigation. Transparency and Accountability of Management of School Operational Assistance Funds (BOS). The strategy for collecting information in the review, especially through meetings and recording, was to obtain information for the previous 3 years. The depth of this review shows that SMAN 12 Makassar has implemented Transparency and Accountability in Management of School Operational Assistance Funds (BOS) well and is developing consistently. This must be seen from the Accountability Report for School Operational Assistance (BOS) Funds in accordance with the special arrangements and Directions for BOS Funds.
Optimizing Breast Cancer Classification: SVM and Random Forest with Hybrid Hyperparameter Tuning and Feature Selection Setiawan, Adil; Soeheri, Soeheri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5720

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the urgent need for early, accurate, and reliable diagnostic support systems. This study proposes an optimized breast cancer classification framework using Support Vector Machine (SVM) and Random Forest (RF) models enhanced through hybrid hyperparameter tuning and feature selection. The Breast Cancer Wisconsin (Diagnostic) dataset, comprising 569 samples with 30 numerical features derived from Fine Needle Aspirate (FNA) examinations, was utilized in this research. Feature selection was conducted using Random Forest feature importance to identify the most relevant diagnostic attributes and reduce dimensionality. Hybrid hyperparameter tuning was implemented using GridSearchCV combined with 5-fold cross-validation to obtain optimal model configurations. Model performance was evaluated using accuracy, malignant-class recall, confusion matrix analysis, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC). Experimental results show that the optimized SVM model achieved significant improvements in accuracy, recall, and ROC–AUC compared to baseline models, indicating enhanced sensitivity and discrimination capability, while the Random Forest model maintained stable performance with marginal gains after optimization. These findings highlight the critical importance of systematic optimization strategies in improving diagnostic safety and reducing false negatives, thereby contributing to the development of more reliable and clinically applicable machine learning-based medical decision support systems.
Co-Authors Abdul Rachman Akbar, Muhammad Barkah Ananda, Dea Ashari, Annisa Aulia, Novi Baehaqi Bagus Riski Dwiki Putri, Dini Ridha Fachry Abda El Rahman Firman Menne Fitri Insani Habib, Nurhayati Jamaluddin, Jaja Kartika, Suci Kholisah, Nur Krismona, Lumi Mane, Arifuddin Manza, Yuke Muhammad Ridwan Muhammad Sadikin Nasution, M. Irfan Aldy Okfalisa Okfalisa Pandere, Perawati Pane, Rahmadani Pratiwi, Dian Irda Rambe, Lima Hartimar Rika Rosnelly, Rika Riski, Bagus Rofiqoh Dewi Rosnaini, Andi Ruri Hartika Zain Sabriadi, Sabriadi Sapiri, Muhtar Siregar, Kiki Putri Ani Soeheri Soeheri, Soeheri Sumijan Sumijan Sumijan, S Suriani, Seri Syahputra, Firman Syamanta, Arbana Tajuddin, Muhammad Utan, Benediktus Ariadi Lolo Yuhandri Yuhandri, Yuhandri