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All Journal Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Sains Dasar Techno.Com: Jurnal Teknologi Informasi Jurnal Pendidikan Fisika Indonesia Jurnal Pendidikan IPA Indonesia Indonesian Journal of Mathematics and Natural Sciences JURNAL FISIKA Sainteknol : Jurnal Sains dan Teknologi Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan Historika Jukung (Jurnal Teknik Lingkungan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pendidikan Matematika (JUDIKA EDUCATION) KOMPUTIKA - Jurnal Sistem Komputer ASEAN Journal on Science and Technology for Development Indonesian Physical Review Integrated Lab Journal Jurnal INFOTEL JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Varia Humanika
Sunarno
Universitas Negeri Semarang, Indonesia
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Simulasi dan Analisis Respon Fuzzy Logic Controller Pada Sistem Suspensi Sunarno Sunarno; Rohmad Rohmad
Jurnal Fisika Vol 5, No 2 (2015)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v5i2.7422

Abstract

Respon keluaran sistem suspensi mekanik pada loop terbuka kadang menghasilkan osilasi yang berlebihan. Keadaan itu akan menghasilkan ketidakstabilan sistem secara keseluruhan. Oleh karena itu perlu dirancang suatu sistem kendali yang memungkinkan tercapainya desain kriteria yang telah direncanakan. Sistem kendali yang relaif baik dalam menghasilkan respon keluaran adalah sistem kendali berbasis logika Fuzzy. Kendali Fuzzy ini dibuat menggunakan simulink Matlab dengan FIS (Fuzzy Inference System) sebagai intinya. FIS ini dibangun menggunakan Fuzzy Logic Toolbox Matlab. Hasil pengujian pada FLC dengan tiga variasi aturan fuzzy yaitu 7 aturan, 25 aturan, dan 49 aturan menunjukkan performansi sistem yang berbeda. Unjuk kerja yang paling optimal dan sesuai dengan desain kriterianya adalah pada jumlah aturan Fuzzy yang terbanyak yaitu 49 aturan. Adapun karakteristik respon keluarannya yaitu settling time = 1.05 sekon, overshoot = 2.78%, peak time = 0.51, rise time = 0.4 sekon serta No steady State Error. 
Analysis of Indonesia's Three Major Anthropogenic Pollutants Which Include Various Emission and Fuel Sectors in the 1990-2015 Period Sunarno, S.; Purwanto, P.; Warsito, B.
Jurnal Pendidikan IPA Indonesia Vol 11, No 2 (2022): June 2022
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v11i2.33224

Abstract

The rapid industrial growth and urbanization in Indonesia over the last two decades have resulted in a significant increase in air pollution, so it has caused a decrease in air quality. An air pollution inventory is needed to determine the level of air quality, emission sector, and the type of pollutant fuel. Air pollutant emission data were obtained from various sources, including Regional Emissions Inventory in Asia (REAS) V3.1, Database Emissions for Global Atmospheric Research (EDGAR) V4.3.2, and Community Emissions Data System (CEDS) V1.0. The data consists of 3 types of emitted pollutants (CO, NOX, and SO2) and two contributing factors (emission and fuel sectors). This study aims to compare data from the emission sources of the three air pollutants, determine the trend of changes in the emission of the three pollutants, and determine the main sectors and fuels that emit the three air pollutants. This research uses the literature study method to collect, visualize, and analyze data. The results showed that between 2005 and 2012, there was a downward trend in emissions in the industrial sector for CO, NOX, and SO2 gases, with the lowest point in August. This is because many industrial sectors have applied the principle of clean energy to reduce air pollution and create clean air. However, the transportation sector showed an increase in CO and NOX emissions and peaked in April and October. Furthermore, the SO2 emissions for the power generation sector fluctuated and peaked in July.
PENERAPAN MACHINE LEARNING DENGAN ALGORITMA SUPPORT VECTOR MACHINE UNTUK PREDIKSI KELEMBAPAN UDARA RATA-RATA Sulistyowati, Indah Dwi; Sunarno, Sunarno; Djuniadi, Djuniadi
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 15 No 1, September Tahun 2024
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.15.1.284-290

Abstract

Machine learning dapat digunakan untuk memprediksi suatu data. Support Vector Machine merupakan bagian dari teknik data mining yang dipergunakan untuk mengidentifikasi dan memprediksi hubungan antara variabel pada suatu dataset. Metode ini efektif untuk melakukan prediksi baik itu untuk klasifikasi ataupun analisis regresi. Perangkat lunak Orange Data Mining 3.3.12 digunakan untuk melakukan proses prediksi. Selanjutnya algoritma Support Vector Machine digunakan untuk memprediksi kelembaban udara. Data masukan adalah suhu, kecepatan angin, penyinaran matahari, dan juga kelembaban udara harian maksimum dan minimum.  Data diambil dari Stasiun Meteorologi Jawa Timur di wilayah Malang pada tahun 2015-2023 sebanyak 2922 dataset. Tujuan penelitian ini adalah untuk mendapatkan nilai RMSE, MAE, dan R-Squared (R2). Rasio perbandingan data pelatihan dan data pengujian ditetapkan pada 70:30. Hasil penelitian menunjukkan bahwa hasil akurasi Root Mean Squared Error (RMSE) dengan nilai 3,378, Mean Absolute Error (MAE) dengan nilai 2,738, dan R-squared (R2) dengan nilai 0,723. Berdasarkan hasil korelasi tersebut menunjukkan bahwa algoritma Support Vector Machine ini termasuk memiliki pengaruh kuat terhadap hasil prediksi kelembaban udara rata-rata harian
Penerapan Algoritma Linear Regression dan Support Vector Regression dalam Prediksi Temperatur Udara di Malang Karnisih, Karnisih; Sunarno, Sunarno; Iqbal, Iqbal; Djuniadi , Djuniadi; Pribadi, Feddy Setio
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.12094

Abstract

Perubahan iklim global dan peningkatan variabilitas cuaca membuat prediksi temperatur udara menjadi salah satu kebutuhan penting di berbagai sektor. Temperatur udara merupakan parameter penting dalam meteorologi yang mempengaruhi berbagai aspek kehidupan manusia. Predisi temperatur udara saat ini banyak memanfaatkan algoritma machine learning, namum nilai akurasi masih belum optimal. Tujuan dari penelitian ini untuk meningkatkan akurasi prediksi temperatur udara rata-rata dengan menggunakan pendekatan berbasis machine learning. Metode dalam penelitian ini menggunakan algoritma Linear Regression dan Support Vector Regression (linier dan gaussian non linear) karena memiliki akurasi prediksi data yang cukup baik di berbagai bidang termasuk bidang hidrologi. Penelitian ini menggunakan data dari Badan Meteorologi Klimatologi dan Geofisika (BMKG) lokasi Stasiun Klimatologi Jawa Timur periode data tahun 2019-2023 dengan parameter cuaca temperatur rata-rata (TAV), kelembaban udara (HAV), kecepatan angin (WAV), curah hujan (RR), tekanan udara (PPP), Penyinaran matahari (SUN) dan titik embun (DEW_POINT). Kinerja model dievaluasi menggunakan pengukuran metrik MSE, RMSE, MAE, MAPE dan R². Hasil pengukuran kinerja model algoritma Gaussian support vector Regression (non linier SVR) lebih baik dibanding dengan linear support vector Regression (linear SVR) dan  algoritma linear regression dengan nilai yang lebih tinggi R² sebesar  0,9891 ± 0,0011 dan nilai error yang lebih rendah pada semua metrik pengukuran. Kata kunci: Prediksi temperatur udara, machine learning, Linear Regression,  Suport Vektor Regression
Prediksi Kabut Menggunakan Recurrent Neural Network dengan Attention Mechanism di Bandara Ruteng Wiujianna, Atri; Pribadi, Feddy Setio; Djuniadi, Djuniadi; Sunarno, Sunarno; Iqbal, Iqbal
Komputika : Jurnal Sistem Komputer Vol. 14 No. 1 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i1.15380

Abstract

Fog phenomena pose a significant challenge in aviation operations, particularly in regions with complex topography such as Ruteng Airport. Thick fog can reduce visibility and increase flight safety risks. This study aims to develop a deep learning-based fog prediction model by comparing the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) enhanced with Attention Mechanism (AM). The dataset consists of 61,187 entries, covering hourly recorded weather parameters over the past ten years (2013–2023). The experimental results show that the addition of Attention significantly improves model performance. The RNN+Attention model emerges as the best-performing model with an accuracy of 0.9981, precision of 0.7755, recall of 0.76, and F1-score of 0.7677, along with the lowest number of False Positives. Meanwhile, the LSTM+Attention model excels in reducing False Negatives, making it suitable for systems prioritizing comprehensive fog detection. Models without Attention demonstrate perfect recall (1.00), but their low precision indicates overfitting. Overall, the integration of the Attention Mechanism enhances the balance between recall and precision and improves model reliability in handling data imbalance. The contribution of this research is that it can serve as a reference for future studies in developing artificial intelligence-based weather prediction models, particularly in addressing fog phenomena. Keywords – Attention Mechanism; Long Short-Term Memory; Fog Prediction; Recurrent Neural Network
Perbandingan Performa Model Long Short-Term Memory dan Bidirectional untuk Prediksi Kabut Wiujianna, Atri; Sunarno, Sunarno; Iqbal, Iqbal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10588

Abstract

Fog is a weather phenomenon that can significantly reduce visibility and impact transportation safety as well as public activities. The Citeko region in Bogor, located in a highland area, experiences a relatively high frequency of fog events, especially during the morning and rainy seasons. This study aims to develop and compare the performance of fog prediction models using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms based on historical weather data from 2013 to 2023. The data, obtained from the Citeko Meteorological Station, includes weather parameters such as dry-bulb temperature, wet-bulb temperature, dew point, visibility, relative humidity, cloud cover, wind direction and speed, and hourly weather conditions. The data underwent several preprocessing steps, including missing value interpolation, fog classification based on weather parameters, normalization, and splitting into training and testing sets (80:20 ratio). The LSTM and BiLSTM models were then trained using a deep learning approach, both with and without early stopping. The results show that BiLSTM with early stopping achieved the best performance: 99.93% accuracy, 96.53% precision, 98.81% recall, and an F1-score of 97.66%, with only 9 false positives and 3 false negatives. This study contributes to the development of fog prediction systems based on artificial intelligence.
DESIGN AND CHARACTERIZATION OF A SIMPLE TEMPERATURE SENSOR BASED ON A POLYMER SINE S-BEND OPTICAL WAVEGUIDE STRUCTURE Yulianti, Ian; Hidayah, Rizki Roqissatul; Leonardy, Joshu; Fianti, Fianti; Sunarno, Sunarno; W. P, Wasi Sakti; Utomo, Galih R.; Prayogo, Defrian; Mufatihah, Nishfa
Indonesian Physical Review Vol. 8 No. 3 (2025)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v8i3.511

Abstract

This study presents the design, fabrication, and performance evaluation of a sine S-bend embedded square-core optical waveguide for temperature sensing applications. The waveguide was fabricated using a straightforward and cost-effective CNC milling technique, with PMMA as the cladding and unsaturated polyester resin (UPR) as the core material. Three different bend heights (0.5 cm, 0.6 cm, and 0.7 cm) were investigated to assess their effects on sensor sensitivity, response time, accuracy, and hysteresis. Results showed that increasing the bend height enhanced the sensor sensitivity, with the highest sensitivity of 0.0283 dB/°C achieved at a bend height of 0.7 cm. The response time was consistently maintained at approximately 40 seconds across all samples. The sensor exhibited excellent accuracy, reaching up to 99.31% at a bend height of 0.5 cm. The maximum hysteresis observed was 0.202 % at a bend height of 0.7 cm, indicating stable performance during thermal cycling. These results confirm that the integration of a sine S-bend structure, smooth core surface, and precise waveguide dimensions can significantly improve sensor performance while maintaining a simple and scalable fabrication process.
Application of Feature Selection and Comparative Analysis of Machine Learning Models for Rainfall Prediction in Jakarta Sulistyowati, Indah Dwi; Sunarno, Sunarno; Iqbal, Iqbal; Syamsuri, KGS M Nurs
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11000

Abstract

Accurate rainfall prediction plays a vital role in reducing disaster risks and supporting public preparedness, particularly in Jakarta where dense population and frequent floods cause serious economic and social impacts. In this study, weather data from the Kemayoran Meteorological Station covering 2004–2023 were analyzed to build rainfall prediction models using machine learning. Three classification algorithms were compared: Logistic Regression, Decision Tree, and Random Forest, selected to represent linear, non-linear, and ensemble approaches. Feature selection was applied using Recursive Feature Elimination (RFE) to identify the most relevant predictors. The models were evaluated using 5-fold cross-validation with metrics including Accuracy, Precision, Recall, F1 Score, ROC AUC, and Cohen’s Kappa. The results indicate that Random Forest achieved the best overall performance with Accuracy of 0.7622, Precision around 0.70, Recall up to 0.63, F1 Score about 0.65, ROC AUC ranging from 0.8044 to 0.8171, and Cohen’s Kappa near 0.48. Logistic Regression also performed competitively with Accuracy of 0.7648, ROC AUC of 0.829, and Kappa of 0.49, while Decision Tree showed lower results with Accuracy of 0.6890 and ROC AUC of 0.6636. The RFE process successfully reduced 18 meteorological attributes to 5 influential features, mainly temperature and relative humidity, which were dominant in distinguishing rainfall events. These findings demonstrate that both Random Forest and Logistic Regression outperform Decision Tree, and Random Forest with RFE can be recommended as the most robust model for rainfall prediction in Jakarta.
PERBANDINGAN PERFORMA MODEL MACHINE LEARNING DALAM PREDIKSI SUHU DI SEMARANG Sutaryani, Apit; Sunarno, Sunarno; Djuniadi, Djuniadi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4884

Abstract

Prediksi suhu udara sangat bermanfaat untuk pertimbangan kebijakan lokal, sepertinya suhu udara lebih tinggi dari tahun-tahun sebelumnya, sehingga perlunya perluasan lahan hijau sebagai strategi untuk memperbaiki kualitas udara dan meningkatkan kualitas hidup masyarakat. Oleh karena itu perencanaan dan implementasi proyek lahan hijau harus menjadi prioritas dalam upaya pengembangan kota yang berkelanjutan. Penelitian ini membandingkan algoritma machine laerning model regresi linier dengan decision tree untuk memprediksi suhu kota Semarang dari tahun 2019 hingga 2023. Variabel data yang digunakan meliputi temperatur, titik embun, kecapatan angin, curah hujan, tekanan udara, lamanya penyinaran matahari, dan kelembaban. Pengolahan data dilakukan menggunakan software rapid miner dengan menggunakan algoritma regresi linier dan decision tree. Hasil penelitian menunjukkan algoritma regresi linier memiliki nilai RMSE sebesar 0.131 +/- 0.000, MAE 0,099 +/- 0,086 dan R2 0.990, sedangkan pada algoritma decision tree nilai RMSE sebesar 0,293 +/- 0,000, nilai MEA 0,189 +/- 0,224 dan nilai R2 0,948. Kesimpulan analisis menunjukkan bahwa algoritma regresi linier lebih akurat daripada decision tree.
Sajian Gendhing Eling-eling untuk Pergelaran Ebeg pada Grup Seni Tradisional Budaya Laras, Sijenggung, Banjarmangu, Banjarnegara Langlang Handayani; Teguh Darsono; Sunarno; Slamet Haryono; R Indriyanto; Indrawan Nur Cahyono; Haydnn Caesha Maulana; Aditya Bagus Wicaksana
Varia Humanika Vol. 5 No. 2 (2024)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/vh.v5i2.14416

Abstract

Penyajian gendhing Eling-eling menjadi elemen penting dalam pergelaran seni pertunjukan tradisional Ebeg, yang membutuhkan pengrawit yang memiliki pengetahuan dan keterampilan yang memadai. Bertolak dari permasalahan grup Budaya Laras yang mengalami kesulitan dalam menyajikan gendhing Eling-eling untuk mengiringi tarian Ebeg yang dimainkan oleh anggotanya, tim pengabdian kepada masyarakat Universitas Negeri Semarang menggelar kegiatan pelatihan dengan model action learning berbasis fasilitasi. Pelatihan dilaksanakan dengan frekuensi sebanyak dua kali pertemuan secara luring di dusun Tempuran, Sijenggung, Banjarmangu, Banjarnegara, yang merupakan basis lokasi grup, dengan peserta para pelaku seni berjumlah 36 orang. Selain bertatap muka, peserta juga melaksanakan tugas dan mendapatkan pendampingan dengan berlatih secara mandiri dalam kegiatan rutin grup. Melalui serangkaian kegiatan pelatihan, peserta menunjukkan hasil berkegiatan berupa performa yang lebih baik dalam memainkan gendhing Eling-eling. Meskipun demikian, dengan memperhatikan penampilan penari yang belum prima selama pelatihan, maka sebagai rencana tindak lanjut akan dilaksanakan pelatihan yang mengarah pada perbaikan performa penari dalam menampilkan tarian Ebeg.
Co-Authors - Sudartono A. Cristina Aditya Bagus Wicaksana Agung Wibowo Bambang Subali D Martina, D Dicko Alyandri Samudra Djuniadi , Djuniadi Djuniadi Djuniadi E Setiowati Edy - Wibowo Feddy Setio Pribadi Fianti Fianti, Fianti Hamidatul Faqqiyyah Haydnn Caesha Maulana Hidayah, Rizki Roqissatul Ian Yulianti, Ian Indrawan Nur Cahyono Iqbal Iqbal Iqbal Isa Akhlis Isa Akhlis Karnisih, Karnisih L Khakim L Lestari Langlang Handayani Leonardy, Joshu Masturi Masturi Masturi Masturi Muchamad Ngafifuddin Mufatihah, Nishfa Musa Abdul Halim N. Hindarto N. Istiana P Purwanto P. Dwijananti Prayogo, Defrian Purwanto - Purwanto R Indriyanto R. Setiawan Rohmad Rohmad Rudi Setiawan S. Rustad, S. S. Susanto, S. Santoso Santoso Slamet Haryono Sugianto Sugianto Sugiyanto - Suharto Linuwih Sulistyowati, Indah Dwi Supriyadi - Supriyadi Supriyadi Susilo - Susilo Susilo Susilo Susilo Sutaryani, Apit Sutikno - Sutikno Sutikno Syamsuri, KGS M Nurs Teguh Darsono Utomo, Galih R. W. P, Wasi Sakti Warsito, B. Wawan Kurniawan Wiujianna, Atri Yohannes Sardjono