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All Journal Semesta Teknika Journal of Electrical Technology UMY Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS)
Karisma Trinanda Putra, Karisma Trinanda
Institut Teknologi Sepuluh Nopember, Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science Other

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Journal : Semesta Teknika

 1 
Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik Syahputra, Ramadoni; Syahfitra, Febrian Dhimas; Putra, Karisma Trinanda; Soesanti, Indah
Semesta Teknika Vol 23, No 2 (2020): NOVEMBER 2020
Publisher : Semesta Teknika

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

Abstract

Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.   This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.   
Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik Syahputra, Ramadoni; Syahfitra, Febrian Dhimas; Putra, Karisma Trinanda; Soesanti, Indah
Semesta Teknika Vol 23, No 2 (2020): NOVEMBER 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v23i2.9940

Abstract

Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.   This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.   
Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations Arrayyan, Ahmad Zaki; Setiawan, Hendra; Putra, Karisma Trinanda
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.21008

Abstract

Depending on persuasive statistics, the increasing prevalence of diabetes worldwide is a huge challenge for individuals, families, and nations. According to International Diabetes Federation (IDF) projections, the number of adults with diabetes is expected to rise by an astounding 46% by 2045, to reach 783 million, or one in eight. In response to this growing concern, this research explores the implementation of the Naive Bayes algorithm for predicting diabetes, employing comprehensive data cleansing and randomization techniques. A systematic evaluation of the model's performance is conducted using several training and testing split ratios (65:35, 75:25, 85:15). The outcome showed that the model performed best at the 65:35 split ratio, with accuracy reaching its maximum of 88.16%, precision 0.883, recall 0.881, and f1-score 0.882.
Co-Authors Agus Jamal Akbar, Gilang Ekavigo Astafil Anjasmara, Fuad Hammaminata Arya Anna Nur Nazilah Chamim Aprilian, Salman Muntaqo Aroffi, Muhammad Rusydi Al Arrayyan, Ahmad Zaki Dedid Cahya Happyanto Era Purwanto Faaris Mujaahid, Faaris Fauzi, Argi Mochamad Hendra Setiawan Indah Soesanti Jamaaluddin Jusman, Yessi Lestari, Sri Indah Masfiyah, Nisfi Nurlailatul Mustar, Muhamad Yusvin Mustika, Doane Puri Nugraha, Vendy Dwi Hendra Purwanto, Kunnu Ramadoni Syahputra Rohmansyah Slamet Suripto Syahfitra, Febrian Dhimas Wardhana, Dimas Aditya Putra Widyasmoro, Widyasmoro Wiyagi, Rama Okta