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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Media Sisfo Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama
WILLY RIYADI
Universitas Dinamika Bangsa, Jambi, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Perancangan Box Penerimaan Paket Berbasis IoT Junaidi, Feri; Jasmir; Riyadi, Willy
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 2 (2024): JAKAKOM Vol 4 No 2 September 2024
Publisher : LPPM Universitas Dinamika Bangsa

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

Abstract

Online buying and selling transactions are different from buying and selling transactions directly. Payment is made with a predetermined payment system and the goods will be sent through the freight forwarder. Freight forwarding services also play a very important role in the online purchase process. Common problems include damage or loss of goods, overpriced shipping costs, and irregular delivery times. Some of the problems mentioned in the delivery of goods are none other than the recipient of the goods themselves. A problem that often occurs is when the recipient is not at home or at the address to which the goods will be delivered. As a result, the goods or packages do not reach the homeowner or the person who ordered the package. From the description of the problem above, the author wants to design a box that can receive packages even though the homeowner is not at home. The system is created so that the package owner can see the condition of the package, both from the shape and weight of the package directly through cameras and sensors, so that the package owner can ensure that the package is received and stored safely.
Perancangan Sistem E-Voting Pemilu Raya Mahasiswa Di Universitas Dinamika Bangsa Sika, xaverius; Pratama, Yovi; Riyadi, Willy; Kisbianty, Desi; Zulia, Restutik
Jurnal Ilmiah Media Sisfo Vol 18 No 2 (2024): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2024.18.2.1990

Abstract

Pemilihan umum Badan Eksekutif Mahasiswa (BEM) di Universitas Dinamika Bangsa (UNAMA) saat ini masih menggunakan sistem manual yang rentan terhadap kesalahan penghitungan, memakan waktu, dan membatasi partisipasi mahasiswa. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut dengan mengimplementasikan sistem e-voting berbasis website menggunakan framework Laravel. Sistem e-voting yang dikembangkan akan terintegrasi dengan Sistem Informasi Akademik (SIAKAD) untuk memverifikasi identitas pemilih dan memastikan setiap mahasiswa hanya memiliki satu hak suara. Laravel dipilih karena kemudahan pengembangan dan fitur-fiturnya yang mendukung pembuatan aplikasi web yang dinamis dan aman. Fitur-fitur utama sistem ini meliputi pendaftaran pemilih secara otomatis dari SIAKAD, proses voting yang sederhana dan cepat, serta penghitungan suara secara real-time. Dengan menggunakan sistem e-voting berbasis Laravel, diharapkan dapat meningkatkan akurasi hasil pemilihan, mempercepat proses rekapitulasi suara, serta meningkatkan partisipasi mahasiswa. Selain itu, sistem ini juga dapat mengurangi biaya penyelenggaraan pemilihan dan meningkatkan transparansi proses pemungutan suara.
Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models Riyadi, Willy; Jasmir; Sika, Xaverius
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1362

Abstract

COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. Data preprocessing was performed using Python's Pandas library. This involved filtering, scaling using min-max normalization, and splitting the data into 80:20 training-testing split using Python's Pandas library. Various optimization techniques—RMSProp, Adam, Nadam, Adamax, AdamW, and Lion—were applied, along with ReduceLROnPlateau, to optimize model performance. The models were evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The best predictive performance was observed in the United States using the CNN-BiGRU model with the Adam optimizer, achieving the lowest MAE of 0.0580, MSE of 0.0097, and MAPE of 0.0979. The use of a balanced dataset, representing each airport's traffic as a percentage of a baseline period, significantly improved prediction accuracy. This research provides valuable insights for stakeholders seeking effective airport traffic prediction methods during unprecedented times.
Ekstraksi Fitur untuk Peningkatan Klasifikasi Teks Komentar Video Youtube Spam Menggunakan Deep Learning Jasmir, Jasmir; Riyadi, Willy; Jusia, Pareza Alam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5249

Abstract

The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments
WORKSHOP NETIKET DAN KEAMANAN DIGITAL BAGI SISWA/I SMA NEGERI 8 KOTA JAMBI Riyadi, Willy; Fachruddin; Kurniabudi
Jurnal Pengabdian Masyarakat UNAMA Vol 4 No 1 (2025): JPMU Volume 4 Nomor 1 April 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2025.4.1.2015

Abstract

Internet telah menyatu erat dengan gaya hidup modern, khususnya bagi kaum muda, Sebagai media yang kaya akan informasi, hiburan, dan sarana komunikasi, internet telah merubah cara kita belajar, bekerja, dan berinteraksi. Di Indonesia berdasarkan Asosiasi Penyelenggara Jasa Internet Indonesia (APJII), penetrasi internet terus meningkat secara signifikan, Fakta ini memperlihatkan adanya ketergantungan yang semakin meningkat pada teknologi digital di tengah masyarakat. Dalam era digital yang semakin kompleks, keamanan digital dan netiket (net etiquette) menjadi isu yang sangat relevan. Hal ini juga sejalan dengan beberapa regulasi yang berlaku di Indonesia, di antaranya Undang-Undang Nomor 11 Tahun 2008 tentang Informasi dan Transaksi Elektronik (UU ITE) serta Undang-Undang Nomor 27 Tahun 2022 tentang Perlindungan Data Pribadi (UU PDP). SMA Negeri 8 Kota Jambi bertempat di Jl. Marsda Surya Dharma Km. 8, Kenali Asam Bawah, Kec. Kota Baru, Kota Jambi, menghadapi tantangan dalam hal kurangnya sosialisasi netiket (net etiquette) berupa aturan sopan santun, etika dan tata krama dalam penggunaan media sosial serta teknik melindungi serta menghindari penyalahgunaan data pribadi saat terhubung ke dunia maya. Workshop ini menjadi upaya konkret dalam mewujudkan siswa menjadi warga digital yang bertanggung jawab dan mampu memanfaatkan teknologi secara bijak.
Perancangan Penyortir Kentang Berdasarkan Ukuran Menggunakan Load Cell Berbasis Arduino Uno Randa, Wahidul; Bustami, M. Irwan; Riyadi, Willy
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 1 (2024): JAKAKOM Vol 4 No 1 APRIL 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2024.4.1.1502

Abstract

Dalam proses penyortiran kentang waktu yang dibutuhkan dalam sortir manual yang memakan banyak waktu, ketidakonsistenan dalam klasifikasi kentang oleh manusia, akurasi pengukuran berat yang kurang terjaga, kesulitan dalam penyortiran kentang dalam skala produksi besar, dan biaya tenaga kerja yang tinggi. Dengan demikian dibutuhkan alat penyortir kentang karena proses penyortiran manual kentang oleh petani memiliki sejumlah kendala, seperti ketidakonsistenan dalam mengklasifikasikan kentang berdasarkan ukurannya, waktu yang dibutuhkan yang cukup lama, tingkat akurasi yang kurang terjaga, serta biaya tenaga kerja yang tinggi. Maka dibuatlah alat penyortir kentang menggunakan teknologi sensor cahaya (LDR) dan sensor load cell untuk memungkinkan penyortiran secara otomatis dan akurat berdasarkan berat dan ukuran kentang. Dengan adanya alat ini, proses penyortiran kentang menjadi lebih efisien, konsisten, dan hemat waktu, sehingga dapat membantu petani dalam meningkatkan produktivitas dan kualitas produk. Penggunaan alat penyortir kentang ini telah menghasilkan perbandingan yang signifikan dibandingkan dengan metode penyortiran manual. Dalam pengujian, alat ini mampu menyortir kentang secara otomatis berdasarkan berat dan ukuran dengan tingkat akurasi yang lebih tinggi, diperkirakan mencapai lebih dari 95% ketepatan dalam mengklasifikasikan kentang. Proses penyortiran menjadi lebih efisien dan cepat, menghemat waktu dan tenaga petani secara signifikan dibandingkan dengan penyortiran manual yang memerlukan upaya yang lebih besar. Dibandingkan dengan penyortiran manual yang cenderung tidak teratur dalam mengklasifikasikan kentang kecil dan besar, alat ini memberikan hasil yang konsisten dan sesuai dengan standar yang telah ditentukan, yang diperkirakan dapat meningkatkan hasil produksi kentang hingga 20-30% atau bahkan lebih. Hal ini juga mengurangi risiko kesalahan manusia dalam pemilihan dan pengukuran berat kentang, sehingga dapat menghasilkan efisiensi yang lebih besar dalam rantai pasokan kentang secara keseluruhan. Dengan demikian, penggunaan alat penyortir kentang ini tidak hanya meningkatkan produktivitas dan kualitas, tetapi juga memberikan manfaat ekonomis yang signifikan dalam pemenuhan permintaan pasar yang semakin meningkat
Perancangan Alat Sortir Buah Durian Berdasarkan Kandungan Gas Dan Berat Dengan Arduino Uno Ali, Edi; Jasmir; Riyadi, Willy
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 1 (2024): JAKAKOM Vol 4 No 1 APRIL 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2024.4.1.1661

Abstract

Indonesia, as an agrarian country with the majority of its population engaged in farming, holds significant potential in durian fruit production, ranking as the third-largest commodity worldwide. Despite high domestic demand for durian, imports remain significant. To enhance production self-sufficiency, this research develops a durian fruit sorting system based on MQ-9 and Load Cell sensors using the Arduino Uno microcontroller. The testing results indicate that the durian fruit sorting system, utilizing MQ-9 and Load Cell sensors with the Arduino Uno microcontroller, has been effectively implemented. Load cell testing demonstrates satisfactory performance in measuring durian weight with an acceptable level of error. The MQ-9 sensor successfully measures ADC values and assesses durian ripeness with adequate accuracy. Servo module testing with PCA9685 shows a good response to Arduino commands. Overall, this system can operate well in the durian fruit sorting process based on weight and ripeness, providing an automated solution to enhance efficiency and accuracy in durian production in Indonesia.
Comparative Analysis of Optimizer Effectiveness in GRU and CNN-GRU Models for Airport Traffic Prediction Riyadi, Willy; Jasmir, Jasmir
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29659

Abstract

The COVID-19 pandemic has posed significant challenges to airport traffic management, necessitating accurate predictive models. This research evaluates the effectiveness of various optimizers in enhancing airport traffic prediction using Deep Learning models, specifically Gated Recurrent Units (GRU) and Convolutional Neural Network-Gated Recurrent Units (CNN-GRU). We compare the performance of optimizers including RMSprop, Adam, Nadam, AdamW, Adamax, and Lion, and analyze the impact of their parameter tuning on model accuracy. Time series data from airports in the United States, Canada, Chile, and Australia were used, with preprocessing steps like filtering, cleaning, and applying a MinMax Scaler. The data was split into 80% for training and 20% for testing. Our findings reveal that the Adam optimizer paired with the GRU model achieved the lowest Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in the USA. The study underscores the importance of selecting and tuning optimizers, with ReduceLROnPlateau used to adjust the learning rate dynamically, preventing overfitting and improving model convergence. However, limitations include dataset imbalance and region-specific results, which may affect the generalizability of the findings. Future research should address these limitations by developing balanced datasets and exploring optimizer performance across a broader range of regions and conditions. This study lays the groundwork for further investigating sustainable and accurate airport traffic prediction models.
Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models Willy Riyadi; Jasmir Jasmir
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3032

Abstract

During the COVID-19 pandemic, airports faced a significant drop in passenger numbers, impacting the vital hub of the aircraft transportation industry. This study aimed to evaluate whether Long Short-Term Memory Network (LSTM) and Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) offer more accurate predictions for airport traffic during the COVID-19 pandemic from March to December 2020. The studies involved data filtering, applying min-max scaling, and dividing the dataset into 80% training and 20% testing sets. Parameter adjustment was performed with different optimizers such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. Performance evaluation uses metrics that include Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The best LSTM model achieved an impressive MAPE score of 0.0932, while the CNN-LSTM model had a slightly higher score of 0.0960. In particular, the inclusion of a balanced data set representing a percentage of the base period for each airport had a significant impact on improving prediction accuracy. This research contributes to providing stakeholders with valuable insights into the effectiveness of predicting airport traffic patterns during these unprecedented times.
Co-Authors Abdul Harris Abdul Harris Abidin, Dodo Zaenal Ali, Edi Amroni Amroni Arvita, Yulia Bustami, M. Irwan Desi Kisbianty, Desi Despita Meisak Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin Ibnu Sani Wijaya ISW Ido Fitra Ramadhan Igra Soliqin Ika Riski Monika Irawan Irawan Irawan, Beni Jasmir Jasmir Jasmir Jasmir Jasmir Jasmir Jasmir, Jasmir Junaidi, Feri Khairuldi Kiki Fauzi Kurniabudi Kurniabudi, Kurniabudi Lies Aryani Lina Juriatun Nasirin M. Irwan Bustami Marrylinteri Istoningtyas Muhammat Andri Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Randa, Wahidul Riko Ardiyanto Rohaini, Eni Rohaini, Eni Selphia , Megah Sika, Xaverius SIKA, XAVERIUS Silvia Rianti Agustini Sutoyo, Mochammad Arief Hermawan Xaverius Sika Yessi Hartiwi Yovi Pratama Yulia Arvita Zulia, Restutik