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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Informatika Komputer UG Journal International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Journal of Social Research Postulat : Jurnal Inovasi Pendidikan Matematika International Journal of Scientific and Professional ICONESTH Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Dina Indarti
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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ANALISIS KEMAMPUAN PEMAHAMAN KONSEP MENGGUNAKAN METODE TOULMIN’S ARGUMENTATION PATTEN DITINJAU DARI GAYA BELAJAR(VAK) Afifah, Kusniatul; Khikmiyah, Fatimatul; Indarti, Dina
Postulat : Jurnal Inovasi Pendidikan Matematika Vol 5 No 2 (2024): Desember 2024
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/postulat.v5i2.7567

Abstract

Penelitian ini bertujuan untuk mengetahui kemampuan pemahaman konsep kelas VIII di UPT SMPN 20 Gresik dalam memecahkan masalah soal benar atau salah menggunakan metode Toulmin’s Argumentation Patten. Jenis penelitian yang digunakan adalah penelitian deskriptif dengan pendekatan kualitatif, dengan subjek penelitian yaitu 3 orang siswa laki-laki kelas VIII-H dari 32 siswa dengan kemampuan sedang yang mewakili tiap type gaya belajar. Teknik analisis data yang digunakan adalah reduksi, penyajian data, dan penarikan kesimpulan. Teknik pengambilan data berupa angket gaya belajar dan wawancara berbasis tugas. Hasil dari penelitian ini menunjukkan bahwa level argument peserta didik dengan gaya belajar kinestetik adalah argumentadi 4, level argument peserta didik dengan tipe gaya belajar Audiotiri adalah argumentasi 1, dan level argument peserta didik dengan tipe gaya belajar Visual adalah argumentasi 2. Berdaarkan hal tersebut peserta didik dengan gaya belajar kinestetik memiliki kemampuan pemahaman konsep lebih baik dari dua type gaya belajar lainnya.
Predicting levels of legal case difficulties using machine learning Sari, Ilmiyati; Kosasih, Rifki; Indarti, Dina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4364-4371

Abstract

Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.
IMPLEMENTATION OF SUPPORT VECTOR REGRESSION FOR PREDICTING LAWYER CHARGES USING CLOUD COMPUTING IN GOOGLE COLAB Ilmiyati Sari; Rifki Kosasih; Dina Indarti
International Conference on Education, Science, Technology and Health (ICONESTH) 2023: ICONESTH
Publisher : International Conference on Education, Science, Technology and Health (ICONESTH)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46244/iconesth.vi.246

Abstract

In court, the role of the lawyer is needed by the defendant in carrying out the defense. In various legal cases, lawyers are able to assist and accompany their clients until the judge's decision is rendered. However, lawyer charge can vary depending on several factors. This research proposes 3 factors that can affect attorney fees. i.e., length of sentence, type of case and distance of lawyer's office to Jakarta district court. The data used in this study is 100 judge's decision data originating from the website of the Jakarta district court. Then the data is divided into two parts with 80 data for training data and 20 for test data. In this study, we predict the lawyer charge by implementing Support Vector Regression (SVR) method in Google Colab. Based on results of this research, we found that the value of Mean Square Error (MSE) was 0.401, the value of Mean Absolute Error (MAE) was 0.347, and the value of Root Mean Square Error (RMSE) was 0.633.
Solar module defects classification using deep convolutional neural network Cahyaningtyas, Rizqia; Madenda, Sarifuddin; Bertalya, Bertalya; Indarti, Dina
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1818

Abstract

Solar modules are essential components of a solar power plant, that are designed to withstand scorching heat, storms, strong winds, and other natural influences. However, continuous usage can cause defects in solar modules, preventing them from producing electrical energy optimally. This paper proposes the development of a deep learning-based system for identifying and classifying solar module surface defects in solar power plants. Module surface condition are classified into five categories: clean, dirt, burn, crack, and snail track. The dataset used consists of 8,370 images, including primary image data acquired directly from the mini solar power plant at the Renewable Energy Laboratory of PLN Institute of Technology, and secondary image data obtained from public repositories. The limitation in the number of images in each category was overcome using data augmentation techniques. The proposed classification model combines Deep Convolutional Neural Networks (DCNN) with transfer learning models (DenseNet201, MobileNetV2, and EfficientNetB0) to perform supervised image classification. Training and testing results on the three models demonstrated that the combination of DCNN + DenseNet201 provided the best performance, with a classification accuracy of 97.85%, compared to 97.25% accuracy for DCNN + EfficientNetB0 and 94.98% for DCNN + MobileNetV2. This research shows that DCNN-based image classification reliably diagnoses solar module defects and supports using RGB images for surface defect classification. Applying the developed system to solar power plant maintenance management can help in accelerating the process of identifying panel defects, determining defect types, and performing panel maintenance or repairs, while ensuring optimal power production.
Exploring Technology Needs to Improve Mental Health Service Coordination Jatnika, Ihsan; Kusumawaty, Ira; Yunike, Yunike; Indarti, Dina; Nugraha, Mara
International Journal Scientific and Professional Vol. 5 No. 1 (2026): December 2025 - February 2026
Publisher : Yayasan Rumah Ilmu Professor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56988/chiprof.v5i1.164

Abstract

Mental health services in Indonesia face significant challenges in coordination between agencies, including mental hospitals, community health centers (Puskesmas), and private institutions. Lack of data integration, limited communication, and low technology utilization hinder service effectiveness. This study aims to explore the need for technology to improve mental health service coordination and identify solutions that can address these challenges. Using a qualitative descriptive approach, the study involved healthcare workers from mental hospitals, community health centers, and private institutions in the provinces of South Sumatra, Lampung, and Jakarta. The results indicate that the implementation of a technology-based integrated information system, telemedicine applications, and intensive training for healthcare workers are key desired solutions. Furthermore, improving inter-agency communication and providing adequate technological infrastructure are also considered important. These findings align with health information systems theory and technology accessibility theory, which suggest that technology can improve coordination and access to services. Despite limitations in terms of sample size and perspective, this study provides important insights into the application of technology in mental health services in Indonesia and suggests solutions to improve the effectiveness of coordination within the mental health service system.
Penerapan Algoritma Support Vector Machine Dalam Pengenalan Wajah Berdasarkan Fitur Isomap Kosasih, Rifki; Mardhiyah, Iffatul; Indarti, Dina
CESS (Journal of Computer Engineering, System and Science) Vol. 11 No. 1 (2026): Januari 2026
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v11i1.68568

Abstract

Pengenalan wajah merupakan salah satu bidang yang digunakan untuk mengenali seseorang melalui citra ataupun video. Pengenalan wajah ini dapat digunakan untuk absensi kehadiran yang lebih efektif dan efisien dibandingkan dengan absensi menggunakan cara manual. Pada penelitian ini data yang digunakan merupakan data citra wajah yang terdiri dari 6 orang dengan tiap orang memiliki 4 variasi ekspresi wajah. Tahapan selanjutnya adalah melakukan ekstraksi fitur wajah dengan menggunakan metode isomap. Metode isomap adalah salah satu metode yang dapat mereduksi dimensi dari dimensi yang tinggi ke dimensi yang lebih rendah. Dalam studi ini dimensi yang dihasilkan sebanyak 4 sehingga terdapat 4 fitur yang akan digunakan dalam pengklasifikasian wajah. Fitur-fitur tersebut dibagi menjadi fitur latih dan fitur uji. Untuk pengklasifikasian wajah, digunakan metode support vector machine (SVM). Metode support vector machine merupakan metode supervised learning yang dapat digunakan dalam pengenalan pola dan klasifikasi. Metode support vector machine memperhatikan perhitungan jarak kedekatan fitur satu dengan fitur lainnya dalam pengenalan pola dan klasifikasi. Berdasarkan hasil klasifikasi diperoleh tingkat akurasi sebesar 87,5%, rata-rata terbobot presisi sebesar 79,1675% dan rata-rata terbobot recall sebesar 87,5%.   
Co-Authors Afifah, Kusniatul Aini Suri Talita Arvianda Iswandari Bertalya Bertalya Cahyaningtyas, Rizqia Dedi Iskandar Desti Riminarsih Fatimatul Khikmiyah Fazriyati, Elyna Gozzal, Reynaldo Mohammad Hutapea, Octaviani Ias Sri Wahyuni Ihsan Jatnika Ilmiyati Sari Ilmiyati Sari Karlina, Oktaviani Ella Lydia Mayasari Mardhiyah, Iffatul Nugraha, Mara Rifki Kosasih Rizki Mufrizal Sarifuddin Madenda Sri Syamsiah Yunike, Yunike