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All Journal Jurnal Sains & Teknologi Modifikasi Cuaca INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi Social, Humanities, and Educational Studies (SHEs): Conference Series Building of Informatics, Technology and Science EAJ (ECONOMICS AND ACCOUNTING JOURNAL) Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (JHSSB) International Journal of Multidisciplinary Research and Literature (IJOMRAL) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Informatics and Electronics Engineering International Journal of Disaster Management Ranah Research : Journal of Multidisciplinary Research and Development Jurnal Ilmu Komputer
Tukiyat Tukiyat, Tukiyat
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Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PELATIHAN DESAIN GRAFIS APLIKASI CANVA UNTUK MENINGKATKAN KREATIVITAS DAN LITERASI DIGITAL BAGI SISWA-SISWI SMK ISLAM PERMATASARI 2 RUMPIN BOGOR Tukiyat, Tukiyat; Sajarwo Anggai; Arya Adhyaksa Waskita; Rafi Mahmud Zain
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 4: September 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i4.8486

Abstract

Pelatihan desain grafis dengan Canva di SMK Islam Permatasari 2 Rumpin Bogor bertujuan meningkatkan kreativitas dan literasi digital siswa. Program ini mengatasi kendala dalam memahami fitur Canva, mengembangkan ide desain, serta keterbatasan akses perangkat dan internet. Metode pelatihan meliputi pengenalan dasar Canva, demonstrasi praktis, dan kolaborasi.Evaluasi menunjukkan 28,57% peserta berasal dari jurusan multimedia dan 71,43% dari teknik komputer dan jaringan, dengan 57,14% laki-laki dan 42,86% perempuan. Sebanyak 80,95% peserta menilai kegiatan sangat baik, dan 84,29% puas dengan penyampaian materi. Kreativitas peserta meningkat dengan 52,38% menilai peningkatan sangat baik, dan literasi digital mencapai 82,54%. Meski demikian, 9,52% merasa pelatihan belum sepenuhnya menumbuhkan daya inovatif. Secara keseluruhan, pelatihan ini berhasil meningkatkan keterampilan desain grafis siswa dengan 81,9% dalam pengembangan kreativitas dan 84,76% dalam penilaian materi. Hasil pelatihan ini memberikan kontribusi positif dalam meningkatkan keterampilan desain grafis dan literasi digital, meskipun masih ada ruang untuk perbaikan dalam interaksi dan bimbingan. Saran untuk program mendatang mencakup evaluasi berkelanjutan, integrasi Canva ke dalam kurikulum, serta peningkatan aksesibilitas teknologi di sekolah. Pelatihan ini tidak hanya meningkatkan kompetensi siswa secara individu, tetapi juga berkontribusi pada peningkatan kualitas sumber daya manusia di era digital.
Contribution of Weather Modification Technology for Forest and Peatland Fire Mitigation in Riau Province Tukiyat, Tukiyat; Sakya, Andi Eka; Widodo, F. Heru; Fadhillah, Chandra
International Journal of Disaster Management Vol 5, No 1 (2022)
Publisher : TDMRC, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/ijdm.v5i1.25372

Abstract

Peat and forest fire have become an annual disaster and one of which is due to low rainfall. The highest insecurity of forest and peatland fires thus occurs in the dry season, where rainfall is very low, and the intensity of the sun is high. The smoke and carbon emitted result in rising air temperatures and cause global warming. Mitigation and control measures before they happen are necessary. Weather Modification Technology (WMT) serves as one of the technological solutions to control forest fires by increasing rainfall in potentially affected locations. This study aims at examining the level of effectiveness of WMT performance in mitigating forest fires in Riau Province conducted in 2020 measured by rainfall intensity, hotspots decreased, and land water level increased. We used descriptive and inferential statistical approaches using Groundwater Level (GwL) measured data as the parameter for forest and land fire mitigation. The flammable peatland indicator is when the water level is lower than 40 cm below the surface of the peatland. In addition, we also utilized rainfall, surface peat water level, and hotspots. The study was conducted in Riau Province from July 24 October 31, 2020. The results showed that the operation of WMT increased rainfall by 19.4% compared to the historical average in the same period. Rain triggered by WMT contributed to maintaining zero hotspots with a confidence level of 80%. The regression analysis of GwL to rainfall (RF) as depicted by Gwl = - 0.66 + 0.001 RF shows a positive correlation between the two. It thus confirms that WMT can be used as a technology to mitigate forest and land fire disasters.
Penerapan Metode Naïve Bayes dan Weighted Product untuk Prediksi Lanjut Studi Peserta Didik Kurnia, Muhammad Dahlan; Tukiyat, Tukiyat; Makhsun, Makhsun
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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Abstract

The low interest of students at the Hidayaturrohman Teluknaga Foundation to continue their education from MTs Hidayaturrohman to Hiro High School makes it necessary to look for the factors causing this lack of interest. This study aims to combine the application of advanced study predictions of students using the naïve Bayes method and the application of ranking with the weighted product method. The data for this research object are graduates of MTs Hidayaturohman in 2022. The research sample is 322 data. The data collection method is in the form of secondary data, namely students graduating from MTs Hidayaturrohman in 2022. The attributes used to assess factors for graduates of MTs Hidayaturrohman to continue their studies at Hiro High School consist of 5 attributes, namely hobbies, modes of transportation, parents' income, distance from home to school and school test scores. In the study, 322 data were divided by 85% (273 data) for training data and 15% (49 data) for testing data. The results showed that the Naïve Bayes method could be applied in predicting the further study of students from MTs Hidayaturrohman to Hiro High School. This is evidenced by the accuracy test using the confusion matrix with an accuracy value of 71%. Where from 49 testing data it is predicted that 34 data with advanced results and 15 data with moving results. Furthermore, data ranking using a weighted product was carried out on 316 data, where 50% of the data (158 data) with the highest vector value v entered advanced ranking and the rest entered moving ranking. The 50% figure is in accordance with the expectations of Hiro High School, namely that as many as 50% of MTs Hidayaturrohman graduates continue on to Hiro High School. Then the highest vector v value is 0.005945284 for parent number 19207207 and the lowest vector v value is 0.001552376 for parent number 19207219.
Sistem Pendukung Keputusan Untuk Menentukan Siswa Calon Peserta Olimpiade Dengan Metode VIKOR dan MOORA Tukiyat, Tukiyat; Rozali, Cristien; Djaoharman, Sulaiman
Jurnal Ilmu Komputer Vol 1 No 1 (2023): Jurnal Ilmu Komputer (Edisi Juni 2023)
Publisher : Universitas Pamulang

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Abstract

Students participation at Paramarta Junior High School for the past several years in the olympic competition has received unsatisfactory result. Achievement achieved seems to be lacking because it does not have a good parameter in the selection of an honor student. The decision to vote is based solely on the results of math teacher’s deliberations. This research was intended to select the best students who will be sent to the Olympic competition in mathematics using the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno) and MOORA (Multi-Objective Optimization on the basic of Ratio Analysis) methods. The research was conducted in Paramarta Junior High School with objects on students as a sample. The study sample selected based on the academic value of grade 7 (seven) of 156 students then selected by selected by the math teacher’s deliberation according to the criteria that have been established by the school before so that 8 students were obtained. Research data in the form of secondary data collected with documentation studies. The data analysis method to select one candidate participant is using VIKOR and MOORA methods. The results that obtained from both methods have the same results, 3 alternavites was selected that is a student named Arya Daffa Khalifahris with 0 VIKOR index value and 0.40 MOORA optimization value.
Prediksi Inflow Daerah Aliran Sungai Larona Dengan Model Seasonal Autoregressive Integrated Moving Average Tukiyat, Tukiyat; Sutrisno, Sutrisno; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

Larona Watershed (DAS) Inflow Prediction entering the reservoir has a very important role in managing the reservoir's water resources. Various approaches using mathematical models have been carried out, the results of which can be used as management tools to understand estimates and predictions of future inflow values, especially in the context of managing and planning water utilization for company needs at PT Vale Indonesia Tbk. The research aims to find a prediction model for the water inflow of the Towuti, Matano and Mahalona reservoirs. The research method uses a statistical approach using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model. Research data, time series data, monthly inflow of the Larona watershed for January 2006 – December 2019. The research results showed that the best model was SARIMA (2,0,1)(0,1,1)12. The mathematical model prediction formulated is 4.786 + 1.459t-1 – 0.648t-2 – 0.714 e_(t-1). The model accuracy level was tested using the RMSE (Root Mean Squared Error) criteria of 0.767, MAE (Mean Absolute Error) level of 0.592, MAPE (Mean Absolute Percentage Error) of 14.58. To validate the predicted values, the F test, Siegel-Turkey, Bartlett, Levene was carried out at the α=5% level. The test results for the difference between actual and predicted values were concluded to accept the null hypothesis, which means that there is no significant difference between the actual data values and the predicted data values.
Pengembangan Sistem Kontrol Pemilah Kematangan Buah Pisang Pada Konveyor Menggunakan Metode Klasifikasi K-Nearest Neighbors Berbasis OpenCV Andrean, Kelvin; Tukiyat, Tukiyat; Susanto, Agung Budi
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

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Abstract

This research focuses on developing a micro-controller-based banana ripeness sorting tool with the implementation of the K-Nearest Neighbors (KNN) algorithm for the classification of ripeness levels based on RGB color image processing using the OpenCV library. Banana is an important fruit in society because of their high nutritional content, but manual sorting of banana fruit is a challenge for farmers and officers. The tool built uses Arduino UNO as a controller, a conveyor belt with a dynamo motor, and a servo motor for sorting. The KNN method is used for classification based on banana skin color. The results showed that the success rate of sorting reached 100% at the neighboring value of K = 3, 93.33% at K = 5, and 86.66% at K = 1. This tool can be an efficient solution for automatically sorting bananas based on ripeness level with high accuracy.
Prediksi Harga Cryptocurrency Menggunakan Algoritma Temporal Fusion Transformer, N-Beats dan Deepar Nugraha Wahyu, Fajar; Anggai, Sajarwo; Tukiyat, Tukiyat
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 1 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i1.1949

Abstract

Cryptocurrency seperti Bitcoin, ETHereum, dan Solana memiliki volatilitas harga tinggi yang menyulitkan prediksi akurat. Penelitian ini bertujuan membandingkan akurasi tiga algoritma deep learning, yaitu Temporal Fusion Transformer (TFT), N-BEATS, dan DeepAR, dalam memprediksi harga harian ketiga aset tersebut. Data penelitian berupa harga penutupan, volume, dan kapitalisasi pasar yang diperoleh melalui CryptoDataDownload. Data diproses menggunakan normalisasi Min-Max Scaling, interpolasi linier untuk missing values, serta feature selection Pearson Correlation. Dataset kemudian dibagi ke dalam data pelatihan, validasi, dan pengujian dengan proporsi yang dapat disesuaikan, sehingga memungkinkan analisis pengaruh perbedaan pembagian data terhadap hasil model. Evaluasi dilakukan menggunakan MAE, RMSE, MAPE, dan R², serta uji statistik untuk menilai perbedaan signifikan antar model. Hasil penelitian menunjukkan bahwa N-BEATS memberikan performa terbaik dengan error paling rendah dan R² tertinggi, sementara TFT berada di urutan kedua dengan hasil yang cukup stabil. Sebaliknya, DeepAR secara konsisten memiliki performa terburuk dengan error tinggi dan R² negatif hampir di seluruh aset. Melalui eksperimen intensif, penelitian ini menunjukkan bahwa N-BEATS mengungguli TFT dan DeepAR dalam menjelaskan variansi data pada ketiga aset kripto: BTC, ETH, dan SOL. Pada semua dataset, N-BEATS mencapai nilai R² positif tertinggi di bawah Konfigurasi 2 (hidden size 32, 4 layers, dropout 0.3), dengan puncak 0.90 pada BTC, 0.93 pada ETH, dan 0.55 pada SOL. Nilai MAPE yang sesuai adalah 2.48% untuk BTC, 4.84% untuk ETH, dan 6.55% untuk SOL. Analisis juga mengungkap bahwa variasi ukuran hidden layer, epoch, dropout, jumlah layer, maupun pembagian data memengaruhi stabilitas serta performa prediksi, namun peningkatan kompleksitas tidak selalu menghasilkan performa yang lebih baik. Dengan demikian, N-BEATS dapat diidentifikasi sebagai model paling efektif untuk prediksi harga kripto, sekaligus memberikan kontribusi teoritis bagi pengembangan model peramalan deret waktu dan kontribusi praktis sebagai acuan bagi investor dalam pengambilan keputusan.
Evaluasi Performa Model Ensemble Learning dalam Deteksi Serangan Jaringan Internet of Things pada Dataset CIC-BCCC-IOT-HCRL-2019 Raharja, Yudi; Susanto, Agung Budi; Tukiyat, Tukiyat
Journal of Informatics and Electronics Engineering Vol 5 No 02 (2025): Desember 2025
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v5i02.1454

Abstract

Perkembangan pesat perangkat Internet of Things (IoT) membawa peningkatan kompleksitas sekaligus risiko pada keamanan jaringan. Studi ini bertujuan untuk menilai performa lima algoritma Ensemble Learning, yaitu Random Forest, AdaBoost, CatBoost, XGBoost, dan LightGBM, dalam Sistem Deteksi Intrusi (IDS) pada jaringan IoT dengan menggunakan dataset CIC-BCCC-IoT-HCRL-2019. Metode penelitian melibatkan tahap pra-pemrosesan data termasuk penerapan dua teknik normalisasi yaitu MinMaxScaler dan Normalizer, serta evaluasi model menggunakan validasi silang 5-Fold Cross-Validation dan pembagian data latih dan uji dengan rasio 80:20. Hasil eksperimen menunjukkan algoritma boosting seperti XGBoost, CatBoost, dan LightGBM secara konsisten memiliki kinerja lebih baik dibandingkan dengan model bagging tradisional seperti Random Forest. XGBoost yang dikombinasikan dengan MinMaxScaler mencapai akurasi tertinggi sebesar 0,9980, sementara LightGBM dengan MinMaxScaler mencatat waktu pelatihan tercepat yakni 2,54 detik. Temuan ini mengindikasikan bahwa penggunaan teknik boosting bersama normalisasi MinMaxScaler dapat secara signifikan meningkatkan akurasi serta efisiensi IDS berbasis IoT. Kata Kunci— Internet of Things, Deteksi Intrusi, Machine Learning, Ensemble Learning, Boosting, Normalisasi Data.
Analisis Prediksi Curah Hujan di Kota Tangerang Menggunakan Metode LSTM dan GRU Supriatna, Dahlan; Anggai, Sajarwo; Tukiyat, Tukiyat
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1068

Abstract

Curah hujan yang tidak menentu dapat memengaruhi berbagai sektor, seperti pertanian, energi, dan infrastruktur. Akurasi prediksi curah hujan sangat penting untuk mitigasi risiko bencana banjir maupun kekeringan. Penelitian ini bertujuan untuk membandingkan akurasi prediksi curah hujan menggunakan dua algoritma deep learning, yaitu LSTM dan GRU serta dapat memberikan kontribusi pada pengelolaan sumber daya air yang lebih efektif. Model ini diterapkan pada data historis curah hujan dan variabel meteorologi terkait, data penelitian adalah data sekunder yang bersumber dari data BMKG Kota Tangerang periode Januari 2014 – Januari 2025 sebanyak 4.062 data. Evaluasi kinerja model dilakukan menggunakan metrik seperti MAE, MSE, RMSE, dan R². Hasil menunjukan Model LSTM dengan konfigurasi hyperparameter optimal—terdiri dari timesteps 36 bulan, 64 unit memori, 100 epoch pelatihan, batch size 16, dropout 0.3, dan learning rate 0.0001—menghasilkan metrik evaluasi terbaik MAE sebesar 0.08473, MSE sebesar 0.00973, RMSE sebesar 0.09863, dan R2 sebesar 0.65601. Nilai R2 yang relatif tinggi ini mengindikasikan bahwa model LSTM mampu menjelaskan sekitar 65.6% dari variabilitas dalam data curah hujan aktual. Sebagai perbandingan, model GRU dengan kinerja terbaiknya (menggunakan batch size 32) menunjukkan metrik evaluasi yang sedikit di bawah LSTM, yaitu MAE 0.08883, MSE 0.01078, RMSE 0.10383, dan R2 Score 0.61878, secara keseluruhan, LSTM terbukti lebih unggul dalam kapabilitas prediksinya.
PREDIKSI SUHU MAKSIMUM BERBASIS DEEP LEARNING DENGAN MODEL LONG SHORT-TERM MEMORY DAN GATED RECCURENT UNIT (STUDI KASUS WILAYAH CIPUTAT) Rahayuningsih, Hesti; Tukiyat, Tukiyat; Rivai, Abu Khalid
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/f9e1d633

Abstract

Prediksi suhu maksimum merupakan hal yang penting dalam kajian meteorologi karena berpengaruh terhadap berbagai sektor, seperti pertanian, kesehatan masyarakat, manajemen energi, dan perencanaan wilayah. Wilayah Ciputat menjadi lokasi yang relevan untuk studi prediksi iklim lokal, karena pernah sebagai wilayah dengan suhu ekstrem sebesar 37,2 °C pada 17 April 2023. Penelitian ini bertujuan untuk membandingkan kinerja model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam memprediksi suhu maksimum harian di wilayah Ciputat serta menentukan model dengan performa terbaik. Data yang digunakan meliputi suhu maksimum harian, curah hujan, kelembapan, dan tekanan udara periode 2009–2025 yang diperoleh dari BMKG Wilayah II Ciputat, sehingga merepresentasikan variabilitas musiman dan pola iklim tropis jangka panjang. Evaluasi model dilakukan menggunakan metrik Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R²), dan Mean Absolute Percentage Error (MAPE). Hasil evaluasi menunjukkan model LSTM dengan performa yang lebih baik dalam memprediksi suhu maksimum dibandingkan model GRU berdasarkan nilai MAE, RMSE, R² dan MAPE. Hasil penelitian ini diharapkan dapat mendukung pengembangan metode prediksi suhu berbasis deep learning yang lebih akurat serta menjadi referensi dalam pengambilan keputusan terkait mitigasi risiko iklim. Kata Kunci: Prediksi suhu maksimum, LSTM, GRU, Deep learning, Ciputat.
Co-Authors A.A. Ketut Agung Cahyawan W Abu Khalid Rivai Achmad Hindasyah, Achmad Agung Budi Susanto Agung Sundoro, Agung Alfan Muttaqin, Alfan Andrean, Kelvin Anggai, Sajarwo Ani Nuraini, Ani Arya Adhyaksa Waskita Brando, Charlo Budi Harsoyo Djaoharman, Sulaiman F Heru Widodo, F Heru F. Heru Widodo, F. Heru Fadhillah, Chandra Gumay, Rizki Izandi Halimatussadiah, Siti iman, Fajar nur Indrajaya, Titus Ismaya, Sakti Brata Kurnia, Muhammad Dahlan Makhsun, Makhsun Maya Sova Murni Handayani Nugraha Wahyu, Fajar Purwadi Purwadi R Djoko Goenawan, R Djoko Rafi Mahmud Zain Raharja, Yudi Rahayuningsih, Hesti Rozali, Cristien Sakya, Andi Eka Satyo Nuryanto, Satyo Sembodo, Eko Sugiyanto - Sunu Tikno, Sunu Supriatna, Dahlan Supriyani, Mei Sutrisno, Sutrisno Taswanda Taryo Tri Handoko Seto, Tri Handoko Uliyatunisa, Uliyatunisa Untung Haryanto, Untung Veithzal Rivai Zainal Waskita, Arya Adhyaksa Widyasari, Lala Wily, Wily Arisandi Zain, Rafi Mahmud