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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 Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) 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.
Optimasi Strategi Promosi Sekolah SMK melalui Segmentasi Data Siswa Baru dengan Clustering Metode K-Means menggunakan Differential Evolution (DE) Hutabarat, Pebruarianto; Setiawan, Adil; Raj, Bill; Prasetyo, M; Irnanda, M. Agung; Gea, Empiter; Johan; Parapat, Andreas
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.779

Abstract

SMK XYZ faces challenges in developing effective and efficient promotional strategies to attract prospective new students. Previously, promotional approaches have been general and failed to address the specific needs of different prospective student segments. This research aims to optimize school promotional strategies by analyzing patterns in new student characteristics through data segmentation techniques. The proposed method is K-Means Clustering optimized with the Differential Evolution (DE) algorithm. DE optimization addresses K-Means' sensitivity to initial cluster center initialization, aiming for more stable and optimal segmentation. The data used includes demographic attributes, major interests, registration pathways, and prior school origins of new students from the 2023/2024 cohort. Research results show that the DE-K-Means combination produces more compact clusters (lower within-cluster sum of squares values) compared to standard K-Means. Based on the resulting cluster analysis, three distinct promotional strategies are formulated for each prospective student segment: digital-intensive approaches, partnerships with feeder schools, and highlighting specific major advantages. Implementing these strategies is expected to significantly increase the quality and quantity of new student admissions.
EKSPLORASI PADA PEMETAAN KLASIFIKASI RADIOGRAF TORAKS PENYAKIT PARU-PARU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Zai, Andreas Rezeki; Suhardi, Bambang; Nowo, Surya Tri; Rosnelly, Rika; Setiawan, Adil
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.7296

Abstract

ABSTRAKAbstrak— Radiograf toraks (CXR) merupakan alat penting dalam diagnosis penyakit paru, namun interpretasinya memerlukan keahlian khusus dan berpotensi menimbulkan bias. Penelitian ini bertujuan mengeksplorasi kinerja lima arsitektur Convolutional Neural Network (CNN) berbasis transfer learning, yaitu VGG16, ResNet50, EfficientNetB0, DenseNet121, dan MobileNetV2, dalam mengklasifikasikan lima kelas penyakit paru-paru: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, dan normal. Dataset yang digunakan dilengkapi dengan preprocessing CLAHE-RGB, augmentasi data, serta penanganan ketidakseimbangan kelas menggunakan class weighting. Evaluasi dilakukan dengan empat skenario epoch (5, 10, 15, dan 30), serta menggunakan metrik akurasi, precision, recall, F1-score, dan confusion matrix. Hasil menunjukkan bahwa model VGG16 pada epoch ke-15 memberikan performa terbaik dengan akurasi 93,95% dan F1-score 0,94. Penelitian ini menunjukkan bahwa kombinasi preprocessing yang tepat dan arsitektur CNN yang sesuai mampu meningkatkan akurasi klasifikasi penyakit paru secara signifikan. Kata Kunci— Convolutional Neural Network, Citra CXR, VGG16, Transfer Learning, CLAHE, Penyakit Paru. ABSTRACTAbstract— Chest radiography (CXR) is a vital tool in diagnosing pulmonary diseases, yet its interpretation often requires expert analysis and may involve subjectivity. This study explores the performance of five Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, EfficientNetB0, DenseNet121, and MobileNetV2 for classifying five categories of lung conditions: bacterial pneumonia, COVID-19, tuberculosis, viral pneumonia, and normal. The dataset underwent preprocessing using CLAHE-RGB enhancement, data augmentation, and class balancing with class weighting. Each model was trained using four epoch scenarios (5, 10, 15, and 30) and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that VGG16 with 15 epochs achieved the best performance, reaching 93.95% accuracy and 0.94 F1-score. This study demonstrates that combining appropriate preprocessing techniques with suitable CNN architectures significantly enhances classification performance for pulmonary disease detection. Keywords— Convolutional Neural Network, CXR images, VGG16, Transfer Learning, CLAHE, Lung Disease.
Co-Authors Abdul Rachman Akbar, Muhammad Barkah Ananda, Dea Ashari, Annisa Aulia, Novi Baehaqi Bagus Riski Bambang Suhardi Dwiki Putri, Dini Ridha Fachry Abda El Rahman Firman Menne Fitri Insani Gea, Empiter Habib, Nurhayati Hutabarat, Pebruarianto Irnanda, M. Agung Jamaluddin, Jaja Johan Johan Kartika, Suci Kholisah, Nur Krismona, Lumi Mane, Arifuddin Manza, Yuke Muhammad Ridwan Muhammad Sadikin Nasution, M. Irfan Aldy Nowo, Surya Tri Okfalisa Okfalisa Pandere, Perawati Pane, Rahmadani Parapat, Andreas Prasetyo, M Pratiwi, Dian Irda Raj, Bill 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 Zai, Andreas Rezeki