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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komunikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Humanitas: Indonesian Psychological Journal Emerging Science Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Cyber Security dan Forensik Digital (CSFD) Management Studies and Entrepreneurship Journal (MSEJ)
Mardhani Riasetiawan
Universitas Gadjah Mada
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media

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Enhancing Diesel Backup Power Forecasting With LSTM, GRU, and Autoencoder-based Input Encoding Dewi, Ni Putu Novita Puspa; Leu, Yungho; Mustofa, Khabib; Riasetiawan, Mardhani
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.92079

Abstract

Ensuring a reliable electricity supply is crucial for Indonesia's development. This study applies deep learning to forecast diesel backup power output. One challenge in such predictions is balancing the input sequence length and the number of features to avoid overly long input sequences, which may degrade model performance. To address this, we utilized an autoencoder to compress the input sequence, improving prediction accuracy. Additionally, given the time-consuming nature of hyper-parameter optimization in deep learning, we employed Bayesian optimization to streamline the process and achieve optimal hyper-parameter settings.The study compares a General Regression Neural Network (GRNN) optimized by FOA with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models optimized by Gaussian Process (GP). Results show that LSTM and GRU with encoded inputs outperform their non-encoded counterparts. The GRU, combined with an autoencoder and Bayesian-optimized hyper-parameters, achieves the lowest prediction error, demonstrating superior forecasting capability.The dataset, obtained from evaluated feeders in Kapuas District, Central Kalimantan, covers hourly power generation and distribution from October 2017 to September 2018. Data was split into 11 months for training and 1 month for testing, with the training set further divided into 70% training and 30% validation. The best performing model achieved RMSE and MAE values of 27.5824 and 14.9804, respectively. Future research may explore further optimization, feature selection techniques, and extended dataset variations.
Analisis Sistem Manajemen Aset Tetap Pada Sektor Publik Berdasarkan Framework Asset Management Landscape Syafaat Ali Akbar; Mardhani Riasetiawan; Achmad Djunaedi
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 6 No. 3 (2025): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v6i3.7537

Abstract

Pengendalian manajemen aset tetap berperan penting untuk mewujudkan efisiensi anggaran belanja modal. Penelitian ini bertujuan untuk melakukan evaluasi terhadap sistem manajemen aset tetap pada tingkat satuan kerja di Kementerian Keuangan berdasarkan salah satu standar manajemen aset internasional yakni Asset Management Landscape dan merekomendasikan inovasi kebijakan guna meningkatkan kualitas sistem manajemen aset tetap. Pendekatan yang digunakan dalam penelitian ini adalah mixed methods. Hasil evaluasi tingkat kematangan subjek manajemen aset terhadap empat belas subjek manajemen pada Asset Management Landscape yang menjadi fokus penelitian menunjukan bahwa subjek asset management planning dan maintenance delivery telah mencapai level competent, sedangkan dua belas subjek lainnya berada pada level developing. Berdasarkan interpretasi lebih lanjut terhadap tingkat kematangan manajemen aset, didapatkan tiga tingkatan subjek manajemen aset. Subjek manajemen aset yang berada pada tingkatan terbawah dari tiga tingkatan tersebut sehingga perlu menjadi perhatian utama dalam rangka meningkatkan kualitas sistem manajemen aset tetap pada tingkat satuan kerja di Kementerian Keuangan adalah data & information management, asset information strategy, management of change dan asset operation. Inovasi kebijakan yang direkomendasikan untuk meningkatkan kualitas empat subjek manajemen aset yang berada pada tingkatan terbawah adalah pengembangan sistem manajemen aset tetap dengan menggunakan Radio Frequency Identification (RFID). Penggunaan RFID dalam sistem manajemen aset tetap bermanfaat untuk meningkatkan aspek akurasi data dalam pelacakan aset tetap dan juga meningkatkan aspek pengamanan aset tetap.
Analisis Topik dan Aktor pada Diskusi GeNose C19 Budiansyah, Andi; Riasetiawan, Mardhani; Djunaedi, Achmad; Rachim, Ilmi Afrizal
Jurnal ILMU KOMUNIKASI Vol. 21 No. 1 (2024)
Publisher : Departemen Ilmu Komunikasi FISIP Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jik.v21i1.7020

Abstract

The GeNose C19 has rised a discussion on Twitter. This study seeks to analyze and categorize topics and actors who have a significant role in spreading knowledge on the usage of GeNose C19 on Twitter between March 1st, 2020, and December 20th, 2021. The findings of this study include various topic, namely pertaining to its mechanisms and operations, superiority, marketing approval, user experience, and product comparison. Actors who played a significant role in spreading knowledge were players from the media, colleges, and government. In addition, state-owned companies play an important part in distributing technical knowledge to the public.
PERANCANGAN DAN IMPLEMENTASI SENSOR NODE UNTUK PEMANTAUAN KUALITAS UDARA SECARA LUAS Latif, Amadeo; Riasetiawan, Mardhani
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 16, No 1 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.105665

Abstract

Polusi udara merupakan masalah lingkungan yang semakin meningkat dan berdampak pada kesehatan manusia serta ekosistem. Pemantauan kualitas udara secara luas menjadi tantangan karena keterbatasan infrastruktur pemantauan yang tersedia. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem sensor node berbasis Wireless Sensor Network (WSN) untuk pemantauan kualitas udara secara luas. Sistem yang dikembangkan menggunakan sensor low-cost untuk mendeteksi polutan seperti PM2.5, karbon monoksida (CO), dan toluene, serta parameter lingkungan seperti suhu dan kelembapan untuk kompensasi pada bacaan sensor MQ135. Data yang diperoleh dikirim ke edge computing module menggunakan protokol komunikasi ESP-NOW dan diteruskan ke cloud melalui jaringan MQTT. Sistem menggunakan sensor MQ7, MQ135, DHT11, dan SHARP GP2Y1010AU0F untuk pengukuran. Hasil pengujian menunjukkan bahwa sistem dapat mengumpulkan dan mengirim dari 80% hingga 90% data yang diharapakan. Algoritma kompensasi yang diterapkan berhasil meningkatkan akurasi pembacaan sensor terhadap perubahan suhu dan kelembapan. Kesimpulan dari penelitian ini adalah bahwa sistem yang dikembangkan mampu melakukan pemantauan kualitas udara secara luas dengan tingkat akurasi yang cukup baik. Namun, beberapa perbaikan masih diperlukan, seperti peningkatan stabilitas komunikasi jaringan dan metode kalibrasi sensor yang lebih akurat.
A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic Riasetiawan, Mardhani; Ashari, Ahmad
Emerging Science Journal Vol. 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-015

Abstract

The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PDF
Co-Authors Achmad Djunaedi Acintya Ratna Priwati Ahmad Ashari Ahmad Ashari Akas Wisnuaji Arlianto Arlianto Avin Fadilla Helmi Budiansyah, Andi Budiansyah, Andi Dedy Hariyadi Dewi, Ni Putu Novita Puspa Hirzen Hasfani Ilmi Afrizal Rachim Irwan Endrayanto, Irwan Itsna Mawadatta Rahma Kartika Sari Khabib Mustofa Latif, Amadeo Leu, Yungho Lukito Edi Nugroho Marvianto, Ramadhan Dwi MHD. Reza M.I. Pulungan Muhammad Asghar Nazal Qamarani, Larasati Syarafina Rachim, Ilmi Afrizal Shaleha, Rinanda Rizky Amalia Syafaat Ali Akbar Tri Febrianto Wiro Santoso Waas