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All Journal NOTARIUS Jurnal Kesehatan Lingkungan indonesia Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Abdimas JAIS (Journal of Applied Intelligent System) Jurnal Teknik Sipil Journal of Information System Majalah Lontar Journal of Development Research JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research AKRUAL: Jurnal Akuntansi FENOMENA ILKOM Jurnal Ilmiah Jurnal Meta-Yuridis Martabe : Jurnal Pengabdian Kepada Masyarakat Voice Of Informatics Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Jurnal Pengabdian Hukum Indonesia (Indonesian Journal of Legal Community Engagement) JPHI JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Ilmu Manajemen dan Akuntansi Terapan Social, Humanities, and Educational Studies (SHEs): Conference Series Indonesian Journal of Advocacy and Legal Services Proceedings of Annual Conference for Muslim Scholars Manajemen Agribisnis: Jurnal Agribisnis Abdimasku : Jurnal Pengabdian Masyarakat Seminar Nasional Hukum Universitas Negeri Semarang Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) IJOIS: Indonesian Journal of Islamic Studies International Journal of Educational Research and Social Sciences (IJERSC) Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Bisnis Mahasiswa An Nawawi Audito Comparative Law Journal (ACLJ) Abimanyu: Journal of Community Engagement Jurnal Multidisiplin Dehasen (MUDE) Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Masyarakat Bhinneka Al-Istinbath: Jurnal Hukum Islam Proceeding International Conference on Malay Identity J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Al-Musyrif : Jurnal Bimbingan dan Konseling Islam Managere: Indonesian Journal of Educational Management Amsir Accounting & Finance Journal JUDIKAT: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Kesehatan Indonesia Scientific Journal of Informatics Indonesian Journal of Advocacy and Legal Services Journal of Business and Information System
Pujiono Pujiono
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro Semarang
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PELATIHAN AUTOCAD DAN RAB UNTUK PENYUSUN RENCANA KERJA PEMERINTAH DESA (RKPD) KECAMATAN PAMONA PUSELEMBA Bangguna, David Lindondaya; Pandoyu, Ebelhart O; Pujiono, Pujiono; Abulebu, Henny I.; Tangkeallo, Marthen M.
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2021): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v4i1.222-226

Abstract

Kecamatan Pamona Puselemba terdiri dari 11 desa yang merupakan salah satu dari 19 kecamatan di Kabupaten Poso Propinsi Sulawesi Tengah. Tahun 2020 jumlah dana desa yang diterima oleh desa sekitar Rp. 960 juta. Tim penyusun Rencana Kerja Pemerintah Desa (RKPD) Kecamatan Pamona Puselemba yang ditunjuk untuk menyiapkan desain dan RAB mengalami kesulitan dalam penyusunannya sehingga Pemerintah Kecamatan Pamona Puselemba bekerjasam dengan Fakultas Teknik Jurusan Teknik Sipil Universitas Sintuwu Maroso Poso untuk melakukan kegiatan pengabdian kepada masyarakat kepada tim penyusun RKPD berupa pelatihan penyusunan desain dan RAB sesuai dengan Standar Nasional Indonesia (SNI). Pelaksanaan pelatihan dilakukan dalam dua tahap yaitu: tahap pertama yaitu pelatihan pembuatan desain bangunan fisik dengan menggunakan AutoCad dan tahap kedua pelatihan pennyusunan RAB menggunakan Standar Nasional Indonesia (SNI). Dari hasil evaluasi menunjukkan bahwa peserta pelatihan dapat memahami seluruh materi yang diberikan, hal ini terlihat dari hasil evaluasi untuk mengukur penyerapan materi oleh peserta dan keberhasilan pelatihan yang dilaksanakan. Kondisi ini terjadi karena rata-rata usia peserta antara 18 – 27 tahun sehingga daya tangkap peserta sangat bagus dan sebagian besar berlatar belakang pendidikan pendidikan peserta lulusan SMK Informatika dan sarjana komputer.
The Relevance of Accounting Information Value and the Portion of Non Public Ownership in Investment Decision Making Sahninda, Berian Putra; Pujiono, Pujiono
Amsir Accounting & Finance Journal Vol. 3 No. 1 (2025): Januari
Publisher : Fakultas Bisnis Institut Ilmu Sosial dan Bisnis Andi Sapada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56341/aafj.v3i1.566

Abstract

The purpose of this research is to analyze the effect of the relevance of the value of accounting information which includes Return on Assets (ROA), Return on Equity (ROE), Earning Per Share (EPS), and Non Public Ownership (KNP) in making investment decisions. The research method used is quantitative method with 15 companies in the agriculture industry sector as the sample. This research uses multiple linear regression techniques as hypothesis testing. The results of the research show that ROA, EPS and Non-Public Ownership are proven to have an effect on the fluctuation of stock prices, while ROE is proven to have no effect on stock prices. The implication of this research is the importance of looking at the size of the KNP and the relevant value of accounting information in the forms of ROA and EPS while still considering the risks associated with the value of ROE in the investment decision-making process
Paralegals as Agents of Legal Empowerment: Bridging Access to Justice and Maqasid al-Shariah in Rural Indonesia Fathurrozi, Adi; Sa'ada, Sri Lum'atus; Pujiono, Pujiono
IJoIS: Indonesian Journal of Islamic Studies Vol. 6 No. 2 (2025): Indonesian Journal of Islamic Studies
Publisher : Civiliza Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/ijois.1516

Abstract

Access to justice for underprivileged communities remains a persistent challenge in many developing countries, including Indonesia, where economic constraints, limited legal literacy, and restricted access to formal legal institutions hinder the realization of legal rights. While prior studies have examined legal empowerment and access to justice, they largely overlook the integration of Islamic normative frameworks, particularly Maqasid al-Shariah, within empirical community-based practices. This study addresses this gap by examining the role of paralegals as agents of legal empowerment in rural contexts and analyzing how their practices bridge access to justice and maqasid-oriented values. Using a qualitative empirical case study design, this research was conducted in Tlogosari Subdistrict, Bondowoso Regency, involving purposively selected participants. Data were collected through in-depth interviews, participant observation, and document analysis, and analyzed using thematic coding based on the Miles and Huberman model. The findings show that paralegals perform multidimensional roles as legal educators, mediators, and facilitators, prioritizing non-litigation mechanisms, participatory legal education, and community-based strategies. These approaches enhance the availability, accessibility, affordability, and effectiveness of legal services for marginalized groups. Moreover, paralegal practices substantively reflect maqasid principles, particularly in safeguarding life, property, and intellect, thereby extending justice beyond procedural dimensions toward human welfare. This study introduces Islamic Social Justice as a novel framework integrating legal empowerment, access to justice, and Islamic ethical principles, offering a context-sensitive and inclusive model for strengthening equitable legal systems.
Penggunaan Model Pembelajaran Kooperatif Tipe Jigsaw untuk Meningkatkan Minat dan Hasil Belajar IPS Siswa Kelas VI SDN 1 Tameng Tahun 2018/2019 Pujiono, Pujiono
Social, Humanities, and Educational Studies (SHES): Conference Series Vol 3, No 4 (2020): Social, Humanities, and Educational Studies (SHEs): Conference Series
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (258.395 KB) | DOI: 10.20961/shes.v3i4.55676

Abstract

The purpose of this study is to explain the learning process using the Jigsaw type cooperative model to increase the interest and learning outcomes IPS of sixth graders at SDN 1 Tameng in 2018/2019. This research is a quantitative research. This research is designed in 2 cycles, each cycle includes stages: planning, implementation, observation, and reflection. The data on the written test of learning outcomes were analyzed until the results could reach the limit of completion, namely a minimum average of 75, a minimum of 80% of students scored 75 or more as the limit for completion of the written test of learning outcomes. The research carried out obtained the results of the average student interest in the initial condition of 59,33%, in the final condition it became 81%. The learning outcomes test in the initial condition was 67.08 with a completeness level of 41.67%, in the final condition, the average was 81.67 with a completeness level of 83,33%. Based on the actions, it can be concluded: The implementation of the learning process according to the steps of the Jigsaw cooperative model can increase the interest and learning outcomes IPS of sixth graders at SDN 1 Tameng in 2018/2019.
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning Jutono Gondohanindijo; Edi Noersasongko; Pujiono Pujiono; Muljono Muljono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29022

Abstract

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Enhancing Diagnostic Accuracy of Polycystic Ovary Syndrome Classification in Ultrasound Images Using a Hybrid Deep Learning Model of VGG16 and AlexNet Maisarah, Hj.; Soeleman, M. Arief; Pujiono, Pujiono; Firdaus, Iqbal; Firdaus, Gusti Aditya Aromatica
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.4932

Abstract

Diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound (USG) imaging still faces a major challenge in the form of inter-observer variability, which can lead to inconsistent diagnostic outcomes and increase the risk of misclassification. This limitation highlights the urgent need for an automated artificial intelligence (AI)–based system capable of performing ultrasound image classification with greater objectivity, accuracy, and consistency. This study aims to develop an automated PCOS classification model based on a hybrid Convolutional Neural Network (CNN) architecture that integrates VGG16 and AlexNet through a feature concatenation mechanism, following preprocessing and data augmentation steps to enhance model generalization. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and specificity as key metrics. Experimental results demonstrate that the VGG16–AlexNet hybrid model achieved the best performance, with an accuracy of 98.26%, precision of 97.90%, recall of 97.90%, F1-score of 97.90%, and specificity of 98.52%. These results outperform other hybrid configurations such as VGG16–MobileNetV2, VGG16–ResNet50, and VGG16–InceptionV3, each of which achieved accuracies above 96%. These findings confirm that combining the feature depth of VGG16 with the computational efficiency of AlexNet enables more comprehensive extraction of spatial and textural patterns in ultrasound images. Consequently, the proposed hybrid model offers a promising AI-driven diagnostic support system that not only enhances the accuracy of PCOS detection but also assists clinicians in making faster, more objective, and consistent medical decisions.
Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM) Urfiyati, Novia; Rijati, Nova; Pujiono, Pujiono; Soeleman, Arief; Firdaus, Iqbal; Nurhuda, Yeni Agus
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.2026.7.3.4966

Abstract

Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.
Optimization of CNN Architectures through Fine-tuning for SIBI Classification Nur Hilmi Insan Muhammad; Abdul Syukur; Pujiono Pujiono
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.2830.378-392

Abstract

This research addresses the computational optimization of convolutional neural network (CNN) architectures for the classification of Indonesian Sign Language System (Sistem Isyarat Bahasa Indonesia, SIBI) static alphabet imagery to enhance digital communication accessibility. Utilizing a domain-specific dataset comprising 1,165 images across 26 alphabet classes, this study tackles the prominent challenges of limited sample sizes and severe class imbalance. We evaluate five state-of-the-art CNN architectures MobileNetV2, DenseNet121, Xception, InceptionV3, and ResNet50V2 under four distinct training data paradigms before and after adaptive fine-tuning. To eliminate predictive bias without pixel-level distortion, oversampling is operationalized via Latent Space SMOTE on flattened vector embeddings, combined with dynamistic runtime image augmentation. The experimental results reveal that MobileNetV2, when optimized through partial layer-freezing (locking 150 baseline layers) under the integrated augmentation and oversampling combination scenario, achieved the highest macro-classification accuracy of 98.30%. This architecture also demonstrated superior efficiency, reducing the computational training latency to 0.53 minutes. The findings underscore the strategic advantage of leveraging optimized lightweight networks like MobileNetV2 for domain-specific visual recognition tasks.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Syukur Abulebu, Henny I. Adhe Irham Thoriq Affandy Affandy Afried lazuardi Ahmad Zainul Fanani Aisyaturrahmi Al Azhar, Cahya Mutiara Alik Katur Rofiah, Binti Alwani, Alwani Amiruddin , Amiruddin Andry Setiawan, Andry Anggun Pambudi Anugroho, Rohmat Aprila, Bord Nandre Arif Hidayat Arif Widagdo Asih Rohmani B. Pakpahan, Irnovia Bayu, Yoni Setyo Nugroho Budi Haryanto Budiono Budiono Busriyanti Busriyanti, Busriyanti Cantika Sari Siregar Catur Supriyanto Chamdan, Umar Chandra, Jennifer Chusnia, Vina Maulidah David S. V. L Bangguna Dedi Dedi Dewi Sulistianingsih Diah Restu Wardani Diah Restu Wardani Dian Anita Nuswantara E. Wuon, Orva Edi Noersasongko Eka Putri, Libryawati Eko Prasetyo Elanda Fikri Estu, Ahmad Zulkarnaen Eti Rimawati Etika Kartikadarma Fadhil, Azhar Fadrul, Fadrul Fahmi Amiq Fathurrozi, Adi Fatihul Barokah Firdaus, Gilbert Firdaus, Gusti Aditya Aromatica Firdaus, Iqbal Fitriyani Fitriyani Fransisca, Luciana Fredy Susanto Freza Surya Asrina Guruh Fajar Shidik Haryati Haryati Herdhianta, Dhimas Hernawan Hadi Heru Agus Santoso Hidayat, Rendra Syarief Holmes Rolandy Kapuy Ibad, Sholihul Iin Purnamasari Imam Bukori Irianti, Lingga Resvita Irma Cahyaningtyas, Irma Ita Mowidu Jutono Gondohanindijo Kahar Kahar, Kahar Kasman Kasman Khoirul Anwar Lintang Venusita Lubis, Lubis Bambang Purnama M. Anwar Sadat M. Tangkeallo, Marthen Made Dudy Satyawan Mahazam Afrad Maisarah, Hj. Merlyana Dwinda Yanthi Mimelientesa Irman Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Moch. Eko Rustiyono Mubarok, Ahmad Hasan Muchammad Shidqon Prabowo Muhamad Haris Zuhri Muhtarom Muljono Muljono Muljono, - Mursalim Nanik Prasetyoningsih Nasution, Khairina Noor Ageng Setiyanto, Noor Ageng Nova Rijati Novitriansyah, Bob Nur Hilmi Insan Muhammad Nurhayati Sitorus Nurhuda, Yeni Agus Nurtantio, Pulung NURUL HIDAYAH O. Pandoyu, Ebelhart Onny Setyawan Pandoyu, Ebelhart O Pandoyu, Ebelhart O. prabawa, randy aditya Pramana, Hendri Julian Pramitasari, Ratih Pratama, Arfian Nanda Yogi Pulung Nurtantio Andono Purwanto Purwanto Purwanto Purwanto Putra, S.E., MSA, Rediyanto Putro, Bagus Prindo Sugihartono R Arief Nugroho Rahman, Fathor Rahman, Sarli Rangkuti, Rahmadsyah Ravindo, Besky Pramudya Rediyanto Putra Rendra Arief Hidayat Retno Sunu Astuti Ridha Rahmawati Rini Fidiyani ROHMAWATI KUSUMANINGTIAS Romi Ilham, Romi Roy Aprianto, Roy Sa'ada, Sri Lum'atus Sahbar, Robi Sahninda, Berian Putra Saputra, Aries Gilang Sari Ayu Wulandari Septian Enggar Sukmana Setiawan, Dicky Sifai, Izzatul Alifah Siti Muslifah Soeleman, Arief Soeleman, M Arief Soeleman, M. Arief Solichul Huda Somad, Agus Sri Slamet Mulyati, Sri Slamet Sudarto Usuli SUGIARTO, LAGA Sugiarto, Triga Agus Sugito - Suharnawi Suharnawi Suharnawi Syahputra, Hidayat Syaiful A. Septemuryantoro Tangkeallo, Marthen M. Teguh Budi Prijanto, Teguh Budi Ujang Nurjaman, Ujang Ulum, Nafi' Fahrur Urfiyati, Novia Wacana, Gitit I.P Wahyu Adi Nugroho Wahyudi, Payzar Wicaksono, Duta Firdaus Widianto, Dhoni WIJAYANTO WIJAYANTO Wuryantoro, Tri Yetri Hasan Yolanda, Oppie Yudistira, Ivan Bhakti Yuli Prasetyo Adhi Yundari, Yundari