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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Technologia: Jurnal Ilmiah Community Development Journal: Jurnal Pengabdian Masyarakat Djtechno: Jurnal Teknologi Informasi Jurnal Abdimas Sang Buana RISTEC : Research in Information Systems and Technology Advance Sustainable Science, Engineering and Technology (ASSET)
Heri Purwanto
Universitas Sangga Buana
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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PENINGKATAN EKONOMI MASYARAKAT BERBASIS BUDIDAYA IKAN KOI SEBAGAI UNGGULAN DESA CIBUBUAN SUMEDANG Heri Purwanto; Edi Andriansyah; Deden Rizal; Bela Ardilla; Fitri Yani Sa’adah; Risma Siti Cahyani
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 4 (2023): Volume 4 Nomor 4 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i4.20018

Abstract

Pemberdayaan masyarakat di Desa Cibubuan, Kecamatan Conggeang, Kabupaten Sumedang merupakan bagian penting dalam pembangunan sumber daya manusia dan masyarakat. Desa ini memiliki populasi sebanyak 2.163 penduduk yang hidup dalam pola kehidupan agraris dengan 814 kepala keluarga dan masih terdapat 91 kepala keluarga pra sejahtera. Kondisi ini menunjukkan adanya tantangan dalam kesejahteraan masyarakat. Meskipun Desa Cibubuan memiliki sumber daya air irigasi yang melimpah dari gunung Tampomas namun pemanfaatannya belum optimal, baru sebatas untuk mengairi sawah dan kolam ikan konsumsi. Disisi lain, kualitas air gunung Tampomas cocok untuk budidaya ikan hias Koi yang memiliki nilai ekonomi tinggi. Oleh karena itu, tujuan dari pengabdian masyarakat ini adalah untuk membantu pengembangan dan pemanfaatan potensi sumber daya tersebut, dengan mengkonversi fasilitas kolam milik warga dari ikan konsumsi ke ikan hias Koi. Metode yang digunakan pada pengabdian masyarakat ini adalah pelatihan dan praktek, sehingga mitra diharapkan mendapatkan best practice dari para praktisi yang didatangkan langsung ke lapangan. Hasil pengabdian masyarakat menunjukan bahwa tingkat pemahaman mitra terhadap aspek kewirausahaan budidaya ikan Koi masih rendah, berdasarkan hasil pre-test hanya 20% dari mitra memiliki pemahaman cukup baik. Setelah dilakukan pelatihan, hasil post-test menunjukkan peningkatan pengetahuan mitra sebesar 95%. Selain itu, pengetahuan dan keterampilan mitra dalam budidaya ikan Koi meningkat signifikan sebesar 80%, hal tersebut mengindikasikan bahwa pelatihan dan praktek budidaya ikan Koi sangat efektif meningkatkan pengetahuan dan keterampilan mitra.
PEMBERDAYAAN TENAGA KEPENDIDIKAN USB YPKP MENUJU PURNABAKTI PRODUKTIF Audita Setiawan; Heri Purwanto; Edi Ardiansyah; Medianto Suryo; Yuli Surya Fauzia Pertami; Rafli Ammar; Dinda Maulidani
Jurnal Abdimas Sang Buana Vol. 4 No. 2 (2023): Jurnal Abdimas Sang Buana - November
Publisher : LPPM Universitas Sangga Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/abdimasusb.v4i2.3009

Abstract

Kegiatan Pengabdian Masyarakat ini dilaksanakan di Universitas Sangga Buana (USB) YPKP untuk pada calon purna bakti tenaga kependidikan. Permasalahan utama yang dihadapi oleh para calon purna bakti adalah kurangnya persiapan yang baik dan matang bagi para calon purnabakti dalam menghadapi masa pensiun padahal usia pada saat mereka memasuki masa pensiun masih merupakan usia yang produktif. Para calon purnabakti akan mengalami momen penting dimana pada saat memasuki masa purnabakti, mereka akan mengalami perubahan peran dan status yang signifikan.  Seiring dengan berakhirnya masa kerja aktif, tenaga kependidikan perlu mempersiapkan diri secara baik untuk menghadapi perubahan tersebut. Persiapan yang matang menjadi kunci untuk menjalani masa purnabakti dengan sukses dan produktif. Kegiatan pengabdian ini dilaksanakan dengan program pemberdayaan wirausaha ikan hias. Pemahaman tentang wirausaha ikan hias dimulai dari keterampilan membudidayakan ikan hias di lahan pribadi yang dimiliki oleh para calon purnabakti sampai dengan bergabung dengan komunitas ikan hias untuk mempermudah proses penjualan ikan hias diharapkan memberikan peluang baru bagi tenaga kependidikan untuk mengembangkan minat dan keterampilan baru yang dapat digunakan sebagai sumber penghasilan.
SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE Agus Nursikuwagus; Suherman; Heri Purwanto; Tono Hartono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7282

Abstract

This research addresses the "rating-content discrepancy" on the Google Play Store, where numerical star ratings often conflict with the actual sentiment of textual reviews. Utilizing the CRISP-DM   framework, the study evaluates the effectiveness of machine learning in resolving these inconsistencies by classifying Instagram user reviews into positive and negative categories. Two primary algorithms were compared using a dataset of 600 reviews. The Support Vector Machine (SVM) model demonstrated high efficacy with an accuracy of 0.84. In contrast, the K-Nearest Neighbors (KNN) model performed poorly, achieving an accuracy of only 0.48. This significant performance gap highlights SVM's superior ability to handle high-dimensional text data through optimal hyperplane separation. The research further integrated the Streamlit library to create an interactive web interface for real-time sentiment prediction and result visualization. Ultimately, this study confirms that a structured CRISP-DM approach combined with SVM provides a robust solution for automated opinion mining, offering a reliable methodology for future data science applications in social media analysis
A CIA-based Sustainable Security Risk Mitigation Model for E-Certificate Systems Teguh Nurhadi Suharsono; John Choi; Raden Ricky Agusiady; Didin Saepudin; Sukadwilinda; Heri Purwanto; Peti Savitri; Ketut Abimanyu Munastha
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2912

Abstract

E-certificates are increasingly adopted across sectors, yet existing studies have not developed an integrated risk mitigation model that combines CIA-based sustainable security with operational and stakeholder perspectives. Current frameworks primarily address isolated technical risks or focus on general PKI security, leaving a gap in holistic modeling tailored to end-to-end e-certificate implementation. This study addresses this gap by proposing a Sustainable Security Risk Mitigation Model for e-certificate systems, guided by the CIA triad—Confidentiality, Integrity, and Availability. A mixed-methods approach was employed, including literature analysis, a Focus Group Discussion (FGD) with industry, government, and academic stakeholders, and expert evaluation using CIA-based scoring on a Likert scale. The empirical data include qualitative perspectives gathered from the FGD and quantitative assessments from expert validation. The proposed model operates in a continuous cycle consisting of risk assessment, mitigation planning, deployment and monitoring, and iterative improvement, ensuring that security controls adapt to emerging threats. Results show that the model achieves an average security validation score (asv) of 4.67, outperforming other existing risk mitigation models in CIA-based evaluation. The findings indicate that institutions can use the model as a practical framework to strengthen e-certificate governance, improve resilience against cyber threats, and support sustainable information security management.
APLIKASI PENGENALAN IKAN HIAS UNTUK PELAJAR MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID Heri Purwanto; Dimas Dwi Maulana; Rudy Sofian; Rikky Wisnu Nugraha; Edi Andriansyah
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6944

Abstract

Indonesia’s marine biodiversity requires early education, yet conventional learning media are less engaging for children. This study develops an Augmented Reality (AR)-based learning application to introduce Indonesia’s ornamental fish interactively. The application features 3D models, allowing children to engage directly with fish visualizations. The Extreme Programming (XP) method was applied with continuous iterations to ensure application quality. Black Box Testing conducted on 30 early childhood learners showed a 40% increase in learning interest compared to conventional media, along with improved understanding of marine biodiversity. The results demonstrate that this application effectively enhances children's interest in learning and raises awareness of marine conservation. Future developments may include additional features such as interactive quizzes and gamification to improve user engagement.
KLASIFIKASI KERUSAKAN MESIN MOTOR MENGGUNAKAN 2D-CNN DENGAN FITUR MFCC DAN SPECTRAL CONTRAST Rudy Sofian; Heri Purwanto; Rikky Wisnu Nugraha; Ghanim Kanugrahan; Alamsyah Wirayudha Hidayat
Technologia : Jurnal Ilmiah Vol 17 No 2 (2026): Technologia (April)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i2.22487

Abstract

Sepeda motor memiliki peran penting dalam mobilitas masyarakat, namun banyak pengguna yang belum mampu mengenali tanda-tanda awal kerusakan mesin, sehingga sering terjadi kesalahan diagnosis saat perawatan. Penelitian ini bertujuan untuk mengembangkan model klasifikasi kerusakan mesin sepeda motor menggunakan 2D Convolutional Neural Network (2D-CNN) dengan fitur ekstraksi Mel-Frequency Cepstral Coefficients (MFCC) dan Spectral Contrast. MFCC digunakan untuk merepresentasikan sinyal audio dalam bentuk dua dimensi, sedangkan Spectral Contrast menonjolkan perbedaan antara puncak dan lembah spektrum. Dataset terdiri atas rekaman suara mesin normal dan mesin dengan kerusakan pada timing chain, masing-masing lima sampel per label. Data melalui tahapan praproses, ekstraksi fitur, augmentasi, pelatihan, dan evaluasi. Hasil pelatihan awal menunjukkan adanya overfitting, dengan akurasi pelatihan mencapai 100% pada epoch ke-8, sementara akurasi validasi hanya sekitar 50%. Setelah dilakukan augmentasi data melalui penambahan noise, pitch shifting, dan time stretching hingga total data menjadi 20 sampel per label, kinerja model meningkat dengan akurasi pelatihan sebesar 94,73% dan hasil validasi yang lebih stabil. Secara keseluruhan, model yang diusulkan mencapai akurasi 75%, dengan kemampuan yang lebih baik dalam mengenali kerusakan timing chain dibandingkan kelas lainnya.
Implementation of Hybrid LSTM-Light GBM and XG Boost Algorithm for Stock Increase Prediction Rudy Sofian; Melly Diyani; Fahmi Reza Ferdiansyah; Heri Purwanto; Rikky Wisnu Nugraha
RISTEC : Research in Information Systems and Technology Vol. 6 No. 2 (2025): RISTEC: Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/3hjjvt46

Abstract

The advancement of machine learning, particularly in time-series analysis, has created significant opportunities for improving the accuracy of stock price prediction. However, the volatile nature of financial markets and the complexity of temporal patterns often hinder the ability of single models to deliver consistent and optimal results. To address these limitations, this study proposes a hybrid approach by integrating three popular algorithms—Long Short-Term Memory (LSTM), XGBoost, and LightGBM—through a stacking ensemble method. The dataset used consists of daily stock prices of Apple Inc. (AAPL) for the period 2014–2024, obtained from Yahoo Finance. The research process includes preprocessing, the construction of time-series datasets using windowing techniques, training of single models, and the application of ensemble stacking. Experimental results reveal that LSTM achieved the best performance among the single models, with a MAPE of 3.70% and R² of 0.9204, demonstrating its ability to capture long-term temporal dependencies. In contrast, XGBoost and LightGBM performed poorly in recognizing sequential patterns, resulting in negative R² values. The combination of all three models through stacking ensemble significantly improved prediction accuracy, achieving a MAPE of 2.57% and R² of 0.9693. These findings confirm that integrating LSTM, XGBoost, and LightGBM not only enhances predictive accuracy but also improves model stability, while contributing to the scientific development of hybrid machine learning methods in stock market analysis.
Co-Authors Agus Nursikuwagus Alamsyah Wirayudha Hidayat Bela Ardilla Deden Rizal R Didin Saepudin Dimas Dwi Maulana Dinda Maulidani Edi Andriansyah Edi Ardiansyah Fahmi Reza Ferdiansyah Fitri Yani Sa’adah Ghanim Kanugrahan John Choi Ketut Abimanyu Munastha Medianto Suryo Melly Diyani Peti Savitri, Peti Raden Ricky Agusiady Rafli Ammar Rikky Wisnu Nugraha Risma Siti Cahyani Rudy Sofian Rudy Sofian Setiawan, Audita Suharsono, Teguh Nurhadi Suherman Sukadwilinda Tono Hartono