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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 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 Kinerja Keuangan Pada PT Pelabuhan Indonesia (Persero) Regional 4 Rachman, Abdul; Sapiri, Muhtar; Setiawan, Adil
ACCESS: Journal of Accounting, Finance and Sharia Accounting Vol. 3 No. 1 (2025): ACCESS: Journal of Accounting, Finace and Sharia Accounting, April 2025
Publisher : Program Studi Akuntasi Universitas Bosowa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56326/access.v3i1.2546

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja keuangan PT Pelabuhan Indonesia (Persero) Regional 4 Makassar periode 2018–2021 berdasarkan standar penilaian kinerja BUMN. Analisis dilakukan melalui delapan rasio keuangan utama yang mencakup aspek profitabilitas, likuiditas, efisiensi, dan struktur permodalan. Hasil penelitian menunjukkan bahwa secara umum kinerja keuangan perusahaan berada dalam kategori cukup baik dan stabil, meskipun terdapat fluktuasi pada beberapa indikator seperti ROI dan TATO. Likuiditas dan efisiensi pengelolaan aset menunjukkan hasil optimal, sementara struktur permodalan memerlukan penguatan. Temuan ini memberikan gambaran bahwa perusahaan telah memenuhi sebagian besar standar kinerja BUMN, namun diperlukan strategi lanjutan untuk meningkatkan daya saing dan keberlanjutan. This study aims to analyze the financial performance of PT Pelabuhan Indonesia (Persero) Regional 4 Makassar for the period 2018–2021 based on the State-Owned Enterprises (SOE) performance assessment standards. The analysis was carried out through eight key financial ratios covering profitability, liquidity, efficiency, and capital structure aspects. The results show that, in general, the company's financial performance is in a fairly good and stable category, although there were fluctuations in several indicators such as ROI and TATO. Liquidity and asset management efficiency showed optimal results, while the capital structure requires strengthening. These findings indicate that the company has met most of the SOE performance standards; however, further strategies are needed to improve competitiveness and sustainability.
Analisis Pengelolaan Anggaran Dan Pendapatan Belanja Daerah Pada Sekretariat Dewan Perwakilan Rakyat Daerah Kabupten Gowa Pandere, Perawati; Suriani, Seri; Setiawan, Adil
ACCESS: Journal of Accounting, Finance and Sharia Accounting Vol. 3 No. 1 (2025): ACCESS: Journal of Accounting, Finace and Sharia Accounting, April 2025
Publisher : Program Studi Akuntasi Universitas Bosowa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56326/access.v3i1.2656

Abstract

Penelitian bertujuan untuk mengetahui bagaimana pelaksanaan manajemen anggaran dan pengeluaran daerah di Sekretariat Dewan Perwakilan Rakyat Daerah (DPRD) Kabupaten Gowa. Objek penelitian ini adalah Sekretariat DPRD Kabupaten Gowa. Metode yang digunakan dalam penelitian ini adalah metode penelitian deskriptif kualitatif, yang bertujuan untuk menggambarkan dan memahami secara mendalam proses pengelolaan anggaran dan pengeluaran di lingkungan Sekretariat DPRD. Hasil penelitian menunjukkan bahwa (1) manajemen pendapatan di Sekretariat DPRD Kabupaten Gowa dapat dikategorikan baik, hal ini terlihat dari tingkat persentase realisasi pendapatan terhadap anggaran yang mencapai 100%. Capaian ini menunjukkan bahwa pendapatan yang direncanakan telah sepenuhnya terealisasi dalam periode anggaran yang berjalan. (2) Pengelolaan pengeluaran di Sekretariat DPRD Kabupaten Gowa juga dapat dikatakan baik, hal ini terlihat dari tingkat persentase realisasi pengeluaran terhadap anggaran sebesar 76%. Meskipun realisasi pengeluaran tidak mencapai 100%, kondisi ini mencerminkan adanya efisiensi dalam penggunaan anggaran karena tidak terdapat pengeluaran yang melebihi anggaran yang telah ditetapkan. Secara keseluruhan, implementasi manajemen keuangan di Sekretariat DPRD Kabupaten Gowa menunjukkan tata kelola yang baik dengan pencapaian pendapatan yang optimal dan penggunaan pengeluaran yang terkendali sesuai dengan ketentuan anggaran yang berlaku. The purpose of this study was to determine how budget management and regional expenditure were implemented at the Secretariat of the Regional House of Representatives (DPRD) Gowa Regency. The object of this study is the Secretariat of the DPRD Gowa Regency. This study employed a descriptive qualitative research method designed to describe and comprehensively understand the process of budget and expenditure management within the DPRD Secretariat. The results of the study show that (1) income management at the Secretariat of DPRD Gowa Regency can be considered good, as indicated by the percentage level of income realization against the budget reaching 100%. This achievement shows that the planned revenues were fully realized during the fiscal year. (2) Expenditure management at the Secretariat of DPRD Gowa Regency is also considered good, as reflected in the percentage level of expenditure realization against the budget of 76%. Although the realization of expenditure did not reach 100%, this condition reflects efficiency in budget utilization, as there were no expenditures exceeding the allocated budget. Overall, the implementation of financial management at the Secretariat of DPRD Gowa Regency demonstrates good governance, with optimal revenue realization and controlled expenditure utilization in accordance with applicable budgetary provisions.
Development of character extraction techniques to detect chicken gender based on egg shape Setiawan, Adil; Yuhandri, Yuhandri; Tajuddin, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1851-1861

Abstract

This research investigates the differentiation of chicken sex based on egg shape images by developing an innovative eccentricity shape feature extraction method. The goal is to determine the sex of chickens before hatching, by identifying the sex of the egg prior to incubation. Images of eggs are captured using a smartphone camera, creating a dataset of 150 images each of male and female eggs, with expert assistance. The research aims to accurately identify male and female eggs, aiding breeders in sorting them. The research introduces a unique method to expand the eccentricity value range, enhancing the precision of egg shape analysis. Characteristic extraction results include: area = 1290194, eccentricity = 6.56, contrast = 0.03, correlation = 0.99, energy = 0.44, and homogeneity = 0.98, with a previous value of 0.72. For Feature Selection, the values obtained are: eccentricity = 0.901188049, Area = 0.73, Energy = 0.03, Contrast = 0.01, Homogeneity = 0.01, and Correlation = 0.01. These findings demonstrate significant improvements in differentiating chicken sex from egg images, showcasing the effectiveness of the newly developed eccentricity shape feature extraction method.
Deteksi Ketidakkonsistenan Font Sebagai Indikator Pemalsuan dan Penyuntingan Dokumen Digital Menggunakan Convolutional Neural Network (CNN) Krismona, Lumi; Ashari, Annisa; Habib, Nurhayati; Setiawan, Adil; Rosnelly, Rika
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 2 (2025): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i2.11741

Abstract

Pemalsuan dan penyuntingan dokumen digital merupakan ancaman serius dalam konteks keamanan informasi dan validitas dokumen resmi. Salah satu contoh aktual adalah kasus pemalsuan dokumen kependudukan untuk Penerimaan Peserta Didik Baru (PPDB) 2024 oleh Ato et al.(Kompas, 2024), telah ditemukan penyalahgunaan data dan perubahan pada dokumen seperti Kartu Keluarga (KK) untuk memanipulasi zonasi pendidikan. Kasus ini menunjukkan bahwa dokumen digital sangat rentan dimanipulasi salah satunya melalui ketidakkonsistenan jenis font pada struktur dokumen digital. Penelitian ini bertujuan untuk mendeteksi secara otomatis terhadap ketidakkonsistenan font dalam dokumen digital menggunakan arsitektur Convolutional Neural Network (CNN). Model dilatih menggunakan 100.000 sampel dari Document Font Recognition Dataset (DTFR), dengan pra-pemrosesan berupa konversi grayscale, normalisasi dan resize citra menjadi 32×32 piksel. CNN dirancang dengan dua lapisan konvolusional, max pooling, dropout dan dense layer. Hasil evaluasi menunjukkan akurasi sebesar 96,85% dengan nilai precision, recall dan F1-score rata-rata sebesar 0,97. Pendekatan ini terbukti lebih akurat dibandingkan metode SVM yang sebelumnya dilaporkan hanya mencapai 94,6%. Penelitian ini menunjukkan bahwa pendekatan CNN efektif untuk mendeteksi ketidakkonsistenan font sebagai indikator awal kemungkinan manipulasi dokumen digital. Meskipun model menunjukkan kinerja tinggi, ruang lingkup penelitian ini masih terbatas pada atribut font bold sebagai indikator utama. Pengembangan selanjutnya dapat mencakup eksplorasi atribut font lain serta validasi pada dokumen dari dunia nyata.
EVALUASI DENSENET-201 UNTUK IDENTIFIKASI BIJI KOPI MENGGUNAKAN HYPERPARAMETER GRIDSEARCH Manza, Yuke; Rambe, Lima Hartimar; Siregar, Kiki Putri Ani; Rosnelly, Rika; Setiawan, Adil
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3898

Abstract

Abstract: Coffee is one of the most important commodities in the global agricultural sector. However, the manual sorting process of coffee beans, which is still widely applied in the Small and Medium Industry (IKM) sector, tends to be time-consuming and often results in inconsistent quality assessments. This study aims to classify coffee bean quality using the DenseNet-201 deep learning architecture, optimized with the GridSearch method to obtain the best combination of hyperparameters. The dataset used consists of 450 images of coffee beans divided into two classes: good-quality and defective beans. The model was trained for 20 epochs using a transfer learning approach and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The test results show that the model before optimization achieved an accuracy of only 78.67%, while the model optimized with GridSearch reached a high accuracy of 99.47% with a low loss value. These findings indicate that the application of DenseNet-201 with hyperparameter tuning is capable of producing accurate and stable classification results, and can be relied upon as an automated solution for sorting coffee beans based on their quality. Keywords: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Coffee Bean Classification Abstrak: Kopi merupakan salah satu komoditas penting dalam sektor pertanian global. Namun, proses pemilahan biji kopi secara manual yang masih banyak diterapkan pada sektor Industri Kecil dan Menengah (IKM) cenderung memakan waktu dan menghasilkan penilaian kualitas yang tidak konsisten. Penelitian ini bertujuan untuk mengklasifikasikan kualitas biji kopi menggunakan arsitektur Deep Learning DenseNet-201 yang dioptimalkan dengan metode GridSearch untuk memperoleh kombinasi hyperparameter terbaik. Dataset yang digunakan terdiri dari 450 gambar biji kopi dengan dua kelas: biji kopi bagus dan biji kopi rusak. Model dilatih selama 20 epoch dengan pendekatan transfer learning dan dilakukan evaluasi terhadap performa model menggunakan metrik akurasi, precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa model sebelum optimasi hanya mencapai akurasi sebesar 78,67%, sedangkan model dengan optimasi GridSearch mampu mencapai akurasi tinggi sebesar 99,47% dan nilai loss yang rendah. Hal ini menunjukkan bahwa penerapan DenseNet-201 dengan tuning hyperparameter mampu menghasilkan klasifikasi yang akurat dan stabil, serta dapat diandalkan sebagai solusi otomatis dalam proses sortasi biji kopi berdasarkan kualitasnya. Kata kunci: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Klasifikasi Biji Kopi
Comparative Analysis: Accuracy of Certainty Factor and Dempster Shafer Methods in Expert Systems for Tropical Disease Diagnosis Yanti, Novi; Insani, Fitri; Okfalisa, Okfalisa; Zain, Ruri Hartika; Setiawan, Adil
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28047

Abstract

Purpose: This study aims to diagnose Neglected Tropical Diseases early by applying the concept of an expert system as a tool that works by mimicking the thought patterns of an expert (doctor). The methods applied in this expert system are Certainty Factor and Dempster Shafer. Both methods work by combining a number of pieces of evidence (symptoms) to produce a confidence value for a disease. Methods: The study began with discussions and interviews with experts to collect information and data about Neglected Tropical Diseases. Conducting a literature review study to enrich knowledge about Neglected Tropical Diseases. Two main inference methods are used to detect diseases based on patient symptoms. The Certainty Factor method uses expert value weighting parameters and patient input value weighting as a basis for knowledge. The Dempster Shafer method only uses expert value weighting in analyzing the probability of symptoms to produce a level of diagnostic accuracy. Result: The Certainty Factor method works by integrating patient and expert weight values into its calculations. Meanwhile, the Dempster Shafer method considers expert weight values without involving patient weight values. Expert system searches using the Forward Chaining inference engine show that the Certainty Factor method has an accuracy probability value of up to 90%. Meanwhile, the Dempster Shafer method has an accuracy value of 70%. Novelty: The results of the study show that expert systems can be applied in the health sector, especially in diagnosing Neglected Tropical Diseases. Of the two methods used, the Certainty Factor method shows a high accuracy value, so it can help detect Neglected Tropical Diseases early and provide treatment solutions to improve health.
Co-Authors Abdul Rachman Akbar, Muhammad Barkah Ananda, Dea Annisa Rahma Ashari, Annisa Aulia, Novi Bagus Riski Dwiki Putri, Dini Ridha Firman Menne Fitri Insani Habib, Nurhayati Jamaluddin, Jaja Krismona, Lumi Manza, Yuke Muhammad Ridwan Muhammad Sadikin Nasution, M. Irfan Aldy Novi Yanti Okfalisa Okfalisa Pandere, Perawati Pane, Rahmadani Rambe, Lima Hartimar Rika Rosnelly, Rika Riski, Bagus Rofiqoh Dewi 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 Yuhandri Yuhandri, Yuhandri