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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Electrices : Jurnal Otomasi Kelistrikan dan Energi Terbarukan Engagement: Jurnal Pengabdian Kepada Masyarakat
Handri Santoso
Pradita University
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Social Sciences

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Word-Level Story Generator Bahasa Indonesia Menggunakan Markov Chain dan Bidirectional GRU Cecilia Angieta Winata; Handri Santoso; Ito Wasito; Haryono .
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5574

Abstract

Story generator plays an important role to help story writers generate story ideas, even initial concepts. Usage of Keras Tokenizer as well as word embedding model requires relatively slower model training speed in order to execute hundreds of training iterations. In this research, we propose design method to create a word-level Indonesian language story generator by implementing Markov Chain model and Bidirectional GRU, which is able to generate quality text as good as the outputs of word embedding models, while having faster model training speed. The performance of Markov Chain-BiGRU model was compared with the performance of word-level BiGRU model and character-level GRU model. The first stage of model evaluation was done by comparing each model’s loss value and model training speed; the second stage was done by giving survey to 33 assessors; while the third stage was done by comparing model’s performance with model from related work. The proposed Indonesian story generator succeeded on increasing the model training speed by 66.38% from related work’s model, as well as producing better-quality text compared to outputs from conventional neural-based and word embedding models.
Efforts to Improve the Welfare of Ornamental Fish Farmers in Kalipaten Village Through the Implementation of LoRaWAN-Based IoT Technology William Widjaja; Theresia Herlina Rochadiani; Handri Santoso; Ninuk Yasmarini; Sherensia Putri Angeliani; Gabriel Alexander
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol 7 No 2 (2023): November 2023
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v7i2.1339

Abstract

The ornamental fish business is currently one of the popular businesses in the community. The relatively fast reproduction cycle, around 0.5 – 1.5 months, with a relatively high selling price, makes this ornamental fish business much in demand by the public. The maximum utilization of technology will help MSMEs in increasing their income. This service activity aims to build a LoRaWAN-based IoT system for ornamental fish farming along with a mobile-based ornamental fish monitoring application to help manage ornamental fish livestock, which ultimately has an impact on improving the quality of ornamental fish and the income of CV Home Aquafish partners. The method utilizes a service-learning approach through stages: Identify, design, and build a LoRaWAN-based IoT and mobile-based monitoring system, implementing, mentoring, and measuring the effectiveness of the LoRaWAN device in improving the quality of ornamental fish and the income of CV Home Aquafish partners. As a result, LoRaWAN can effectively help minimize mortality in ornamental fish seedlings so that the quality of the fish is maintained. The income of CV Home Aquafish's ornamental fish nursery partners in Kalipaten Village, Gading Serpong, Tangerang also increases.
GenAI as an IoT programming assistant: a case study on automated debugging for air quality monitoring systems Steven Imanel Bawole; Handri Santoso
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27707

Abstract

The rapid expansion of internet of things (IoT) technology has necessitated the development of user-friendly programming solutions for non–experts. While generative artificial intelligence (GenAI) offers the potential to democratize code development, its ability to assist in the intricate task of automated debugging, particularly regarding hardware integration remains a critical area of research. A design research approach was employed, employing a structured four – phase workflow: error analysis, diagnostic execution through prompting, iterative solution analysis, and functional verification. The methodology was applied to an experimental case study involving an air quality (AQ) monitoring system. The study tested the artificial intelligence (AI)’s capacity to debug C++ code intended for the Arduino integrated development environment (IDE). Gemini AI successfully identified and resolved three critical logic errors arising from mismanaged MQ135 calibration variables, incorrect loop sequencing, and data desynchronization between the organic light emitting diode (OLED) display and the internal status logic. GenAI proved effective as a programming assistant for resolving bugs in IoT applications. However, effective debugging still depends on well-structured prompts and a basic understanding of the underlying IoT hardware.
Model CNN Ringan (Lightweight CNN) untuk Klasifikasi Sampah Sungai pada Raspberry Pi Handri Santoso; Ama Muzni Mahmudi
Electrices Vol 8 No 1 (2026): Volume 8 Nomor 1 Tahun 2026
Publisher : Jurusan Teknik Elektro, Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pencemaran sungai akibat sampah terapung menjadi permasalahan serius yang berdampak pada lingkungan, drainase, dan kesehatan masyarakat. Pemantauan konvensional masih bergantung pada inspeksi manual yang mahal, lambat, dan tidak berkelanjutan. Penelitian ini mengusulkan sistem Artificial Intelligence of Things (AIoT) untuk klasifikasi sampah terapung secara real-time menggunakan perangkat edge berbiaya rendah. Dataset dikumpulkan dari November 2024 - Juli 2025 dari sungai dan kanal di daerah Tangerang dan sekitarnya, yang terdiri dari 22.333 anotasi yang diproses menjadi 7.000 citra objek seimbang dalam tujuh kelas sampah: e-waste, fiber/paper, kaca berwarna, kaca bening, organik, plastik keras, dan plastik lunak. Tiga model lightweight CNN dibandingkan, yaitu MobileNetV2, EfficientNetB0, dan MobileNetV3Small. Hasil pelatihan menunjukkan MobileNetV2 memberikan performa terbaik dengan akurasi validasi 94,1% dan konvergensi stabil. Model terbaik kemudian diimplementasikan pada Raspberry Pi menggunakan TensorFlow Lite serta Coral Edge TPU. Pengujian pada 1.400 citra menunjukkan inferensi CPU mencapai akurasi 94,36% dengan latensi 102,59 ms/gambar, sedangkan Coral Edge TPU mencapai akurasi 93,93% dengan latensi 5,93 ms/gambar atau 168,69 gambar/detik. Hasil ini membuktikan bahwa sistem AIoT berbasis lightweight CNN efektif untuk pemantauan sampah sungai secara real-time.
Co-Authors Ama Muzni Mahmudi Cecilia Angieta Winata Gabriel Alexander Haryono . Ito Wasito Ninuk Yasmarini Sherensia Putri Angeliani Steven Imanel Bawole Theresia Herlina Rochadiani William Widjaja