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All Journal Jurnal Teknologi Industri Pertanian Teknotan: Jurnal Industri Teknologi Pertanian Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Aplikasi Teknologi Pangan Indonesian Journal of Chemistry IKRA-ITH Humaniora : Jurnal Sosial dan Humaniora JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal RESISTOR (Rekayasa Sistem Komputer) IKRA-ITH ABDIMAS Pontianak Nutrition Journal (PNJ) Journal of Industrial and Information Technology in Agriculture Canrea Journal: Food Technology, Nutritions, and Culinary Journal Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Food ScienTech Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Decode: Jurnal Pendidikan Teknologi Informasi SN-Biosper Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Pontianak Nutrition Journal (PNJ) Composite Jurnal Sains dan Teknologi Pangan Jurnal Komtika (Komputasi dan Informatika) Global Science: Journal of Information Technology and Computer Science
Yana Cahyana
Fakultas Teknologi Industri Pertanian, Universitas Padjadjaran
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Implementasi Clustering Menggunakan Algoritma K-Means dan K-Medoids pada Kerusakan Tempat Tinggal Akibat Bencana di Jawa Barat Nurani Khoerunnisa; Amril Mutoi Siregar; Yana Cahyana
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Bencana alam adalah rangkaian peristiwa yang mengganggu dan mengancam keselamatan serta menyebabkan kerugian materiil dan nonmateriil, terutama di Provinsi Jawa Barat. Dampak dari bencana alam tersebut menyebabkan banyak masyarakat kehilangan tempat tinggal mereka. Hal ini menimbulkan kekhawatiran masyarakat akan keamanan daerah tempat tinggal mereka. Berdasarkan permasalahan tersebut, penelitian ini menghitung cluster kerusakan tempat tinggal di Jawa Barat menggunakan algoritma K-Means dan K-Medoids Clustering untuk mengelompokkan kabupaten atau kota di Jawa Barat. Sebanyak 27 kabupaten atau kota di Provinsi Jawa Barat dikelompokkan ke dalam 2 cluster, yaitu cluster Tinggi (rawan) dan cluster Rendah (aman), berdasarkan dataset yang diperoleh dari situs web Badan Penanggulangan Bencana Daerah (BPBD) dengan jumlah data sebanyak 1.620. Hasil penelitian menunjukkan bahwa algoritma K-Means lebih optimal, dengan jumlah daerah dalam cluster Rendah (aman) sebanyak 14 dan dalam cluster Tinggi (rawan) sebanyak 13. Sementara itu, algoritma K-Medoids menghasilkan 15 daerah dalam cluster Rendah (aman) dan 12 daerah dalam cluster Tinggi (rawan). Evaluasi menggunakan silhouette coefficient menunjukkan bahwa algoritma K-Means lebih unggul dengan nilai 59% (0.59), dibandingkan dengan algoritma K-Medoids yang memiliki nilai 58% (0.58).
Penerapan Algoritma Convolutional Neural Network (CNN) untuk Deteksi Telur Bebek Fertil dan Infertil Ricky Steven Chandra; Hanny Hikmayanti Handayani; Yana Cahyana
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Dalam penetasan telur bebek, telur yang infertil perlu disortir dari mesin tetas agar tidak membusuk di dalamnya. Proses penyortiran umumnya dilakukan dengan meneropong telur menggunakan senter atau lampu yang diletakkan di balik telur. Tujuan dari penelitian ini adalah untuk mengembangkan sistem deteksi telur bebek fertil dan infertil menggunakan algoritma Convolutional Neural Network (CNN) guna menggantikan peneropongan secara manual, karena tingkat kelelahan manusia dapat menyebabkan kesalahan dalam penyortiran telur bebek fertil dan infertil. Model yang digunakan dalam penelitian ini adalah You Only Look Once (YOLO), yang merupakan salah satu model deep learning yang efektif untuk pengenalan objek. Penelitian ini terdiri dari beberapa tahap, yaitu pengumpulan data, pra-proses data, konfigurasi jaringan YOLO, pelatihan model YOLO, dan pengujian. Jumlah data citra yang digunakan dalam penelitian ini sebanyak 800, yang terdiri dari dua jenis telur bebek, yaitu fertil dan infertil. Hasil pengujian yang dilakukan sebanyak 40 kali menunjukkan bahwa dengan menggunakan algoritma Convolutional Neural Network (CNN), akurasi yang dicapai dalam membedakan telur bebek fertil dan infertil mencapai sekitar 95%. Hasil ini menunjukkan bahwa CNN memiliki potensi yang signifikan dalam membedakan telur bebek fertil dari infertil, serta memberikan solusi yang cepat dan efisien bagi peternak bebek.
Analisis Sentimen Kenaikan Harga Bahan Bakar Minyak Menggunakan Algoritma Naïve Bayes dan Support Vector Machine Rizka Ayu Permana; Yana Cahyana; Adi Rizky Pratama
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Bahan Bakar Minyak (BBM) memiliki peranan penting dalam kehidupan masyarakat, karena harga BBM mempengaruhi harga komoditas dan sektor lainnya. Kenaikan harga BBM sering menimbulkan pro dan kontra di kalangan masyarakat. Untuk melihat bagaimana masyarakat merespons kenaikan harga BBM, salah satunya dapat dilakukan melalui analisis media sosial seperti Twitter. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap kenaikan harga BBM dengan menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM). Proses dimulai dengan crawling data tweet menggunakan kata kunci "harga BBM naik". Data yang terkumpul kemudian dibagi menjadi dua kelas, yaitu kelas positif dan kelas negatif. Data tersebut selanjutnya melalui proses preprocessing yang meliputi cleaning, case folding, tokenizing, stopword removal, normalize, dan stemming. Pembagian data dilakukan dengan 70% untuk data training dan 30% untuk data testing. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes memperoleh akurasi sebesar 78,3%, precision 99,2%, dan recall 75,1%. Sementara itu, algoritma Support Vector Machine (SVM) memperoleh akurasi 92,5%, precision 93,0%, dan recall 98,5%.
Prediksi Harga Saham Bank Rakyat Indonesia Menggunakan Algoritma Linear Regression dan Support Vector Regression Rizki Nur Annisa; Sutan Faisal; Yana Cahyana
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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nvestasi saham merupakan salah satu investasi jangka panjang yang dapat dilakukan oleh masyarakat untuk melindungi risiko biaya, terutama dalam mempersiapkan keuangan masa depan. Berinvestasi dalam saham dapat memberikan dividen yang cepat dan cukup besar bagi investor. Selain memberikan keuntungan, terdapat berbagai faktor yang dapat memengaruhi naik atau turunnya harga saham, seperti kondisi dan kinerja perusahaan, risiko, dividen, suku bunga, kondisi ekonomi, peraturan pemerintah, dan tingkat penurunan. Untuk meminimalkan risiko kerugian bagi investor saat mengambil keputusan investasi, diperlukan analisis secara fundamental dan teknikal. Namun, sebagian orang tidak terlalu paham mengenai cara menganalisis perusahaan secara mendalam. Oleh karena itu, cara yang lebih mudah adalah dengan melakukan prediksi dan analisis pergerakan saham. Penelitian ini bertujuan untuk melakukan prediksi harga penutupan saham Bank Rakyat Indonesia (BRI) menggunakan algoritma Linear Regression dan Support Vector Regression (SVR). Tujuan dari penelitian ini adalah untuk memilih algoritma yang paling cocok untuk memprediksi harga saham sebagai rekomendasi bagi investor. Hasil penelitian menunjukkan bahwa baik algoritma Linear Regression maupun SVR dapat digunakan untuk memprediksi harga saham. Namun, nilai error RMSE pada algoritma Linear Regression sebesar 69.920, sementara pada algoritma SVR sebesar 69.924. Berdasarkan hasil ini, dapat disimpulkan bahwa algoritma Linear Regression memiliki performa yang sedikit lebih baik dibandingkan dengan SVR, dengan selisih nilai error sebesar 0.004.
Penerapan Metode Naive Bayes Multinomial dan Complement dalam Membandingkan Tingkat Akurasi terhadap Analisis Sentimen Kurikulum Merdeka Wenda Adi Kusnaya; Yana Cahyana; Ayu Ratna Juwita
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Kementerian Pendidikan telah mengeluarkan program Kurikulum Merdeka di lingkungan sekolah, yang memungkinkan siswa untuk mengembangkan minat, bakat, dan keterampilan mereka sehingga dapat lulus dengan kesiapan menghadapi dunia kerja atau pendidikan tinggi. Namun, program ini telah memicu berbagai tanggapan di Twitter, menciptakan kontroversi yang perlu dijelaskan. Untuk menganalisis sentimen terkait Kurikulum Merdeka, penelitian dilakukan menggunakan algoritma Naïve Bayes Multinomial dan Complement. Dalam penelitian ini, total 627 data yang telah diberi label dan diproses sebelumnya digunakan sebagai dataset. Dataset ini kemudian dibagi menjadi dua, yaitu 80% untuk data latih dan 20% untuk data uji. Metode evaluasi yang digunakan adalah confusion matrix. Hasil evaluasi menunjukkan tingkat akurasi sebesar 89% untuk algoritma Naïve Bayes Multinomial dan 88% untuk Complement. Kesimpulannya, algoritma Naïve Bayes Multinomial memberikan tingkat akurasi yang lebih tinggi dalam menganalisis sentimen terkait Kurikulum Merdeka.
Model Machine Learning Untuk Analisis Sentimen Masyarakat Terhadap Kenaikan PPN di Media Sosial X Ridho Pratama, Ilham; Cahyana, Yana; Rahmat; Wahiddin, Deden
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.523

Abstract

This study examines people's reactions to the Indonesian government's plan to adjust the VAT rate from 11% to 12%, which is scheduled to take effect in 2025. This policy triggered a variety of opinions among netizens, especially on the social networking service X. To explore public opinion, data was collected through web crawling techniques from October to December 2024, resulting in 1,871 records. Then the dataset was preprocessed by text cleaning, case folding, tokenization, stopword removal, and stemming, and the dataset was reduced to 1806. In addition, up to 1000 data will be manually labeled, negative, neutral, positive, by language experts to ensure that each sentence has the appropriate label. These data are used for testing and training, then up to 806 unlabeled data are used as final testing. At the word weighting stage, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used to perform the process. In this study, three machine learning algorithms were used to compare the classification performance, namely Support Vector Machine (SVM), Random Forest, and Decision Tree. Based on the evaluation results, the SVM algorithm recorded the highest accuracy rate of 94%, followed by Random Forest with 93% and Decision Tree with 91%. The results showed a predominance of negative sentiments, indicating public dissatisfaction with the policy. This study proves that machine learning techniques can be effectively used to capture public perceptions through social media, which in turn can be a benchmark for the government to make decisions that will be enforced.
Analisis Sentimen Masyarakat Terhadap Pembatasan BBM Pertalite Menggunakan Random Forest dan K-Nearest Neighbor Muhammad Fadillah, Farhan; Cahyana, Yana; Rahmat; Fauzi, Ahmad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.547

Abstract

This study aims to analyze public opinion regarding the policy of limiting the use of Pertalite fuel by examining user comments on the Instagram platform. To classify these opinions, classification approaches using K-Nearest Neighbor (KNN) and Random Forest algorithms were employed. Comments were categorized into three sentiment expressions: positive, negative, and neutral. The research stages included data collection (crawling), text cleaning and normalization, sentiment labeling, weighting using the TF-IDF technique, model development, and performance evaluation. A total of 2,081 comments were used, with 1,000 comments labeled by language experts as training data, and the remaining used for testing. Model evaluation was conducted using two data splitting ratios, 80:20 and 70:30, to assess classification stability and accuracy. The results indicate that the Random Forest algorithm consistently outperforms KNN, achieving the highest accuracy of 73% under the 80:20 scenario. The classification distribution suggests a dominance of negative sentiment in public opinion toward the policy. These findings reflect public dissatisfaction and serve as critical input for the government in reviewing the subsidized fuel distribution policy. This research also highlights the potential of social media as an alternative data source for real-time public perception analysis.
Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis Iskandar, Muhammad Irsyad; Mudzakir, Tohirin Al; Cahyana, Yana; Pratama, Adi Rizky
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.554

Abstract

The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
Development of AI-Based Public Safety System with Face Recognition Using CNN and SVM Models in Real-Time Alifa, Naila Ratu; Yana Cahyana; Rahmat, Rahmat; Sutan Faisal
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9524

Abstract

Sexual crimes are an increasing problem, with many cases difficult to identify due to the limitations of existing surveillance systems. This study aims to develop an Artificial Intelligence (AI)-based system using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for gender identification in order to support sexual crime investigations. The methods used include processing facial image datasets, training models using CNN for feature extraction, and SVM for gender classification. The results showed that the CNN model achieved an accuracy of 90.15%, while the SVM model only achieved an accuracy of 82.16%. Further evaluation with a confusion matrix showed that CNN was more accurate in classifying gender than SVM. With these results, the developed system has the potential to help authorities identify perpetrators of sexual crimes more quickly and accurately. The dataset used consists of 23,706 grayscale facial images of 48x48 pixels, with a balanced distribution of male and female samples. The CNN architecture includes three convolutional blocks and achieves 90.15% accuracy. Although designed for real-time operation, inference speed needs further validation using FPS or latency metrics on specific hardware platforms.
Public Sentiment Analysis on the Boycott Israel Movement on Platform X Using Random Forest and Logistic Regression Algorithms Agustin, Rachmayanti Tri; Cahyana, Yana; Baihaqi, Kiki Ahmad; Rohana, Tatang
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9551

Abstract

This research aims to analyze public sentiment toward the boycott movement against Israel on the X platform by applying Random Forest and Logistic Regression algorithms. The study uses 616 tweets collected through web crawling with relevant keywords such as "Boikot", "Israel", and "Palestine", covering the period from March 1, 2023 to January 30, 2025. The dataset underwent preprocessing including cleaning, normalization, stopword removal, tokenization, and stemming. Sentiment labeling was conducted both manually, categorizing the data into positive, negative, and neutral classes. TF-IDF was used for feature weighting. The data was split into 80% training and 20% testing. The Random Forest model achieved an accuracy of 70%, while Logistic Regression reached 68%. Both models showed higher accuracy in predicting positive sentiment compared to negative and neutral. The results suggest that public opinion on the boycott movement on social media tends to be supportive, with “Boikot,” “Israel,” and “Palestine” being the most dominant terms. Random Forest performed slightly better in classification, though improvements are needed in recognizing non-positive sentiments.
Co-Authors Abda Abda Abdullah Darussalam Addion Nizori Adi Rizky Pratama Adi Susilo Aenul Fuadah Agung Triatna Agustin, Rachmayanti Tri Ahmad Fauzi Alifa, Naila Ratu Ambarwati, Evi Karlina Amid Rakhman amril siregar Anisa Itiawanti Annisa Nurhalizah Aqib Zhaky Arum Galih Pertiwi Awal, Elsa Elvira Ayu Juwita Baihaqi, Kiki Ahmad Banafshah Shafa Bramandito Affandi Budiyanto Budiyanto Deden Wahiddin Dewi, Indah Purnama Didik Remaldhi Direja, Azhar Ferbista Duhita D Utama DWI KUSUMANINGRUM Een Sukarminah Efri Mardawati Enjelia, Lola Faisal, Sutan Fauzan Azima Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Fitria, Denisa Gumilar, Rizki Bintang Hanan, Sofiah Marwah Hanny Hikmayanti Handayani Hartono Wijaya, Sony Heri Hermawan Herlina Marta Hilda Novita Humaryanto, Humaryanto Iis Sadiah Imas Siti Setiasih In-In Hanidah Indira Lanti Kayaputri Indra Lasmana Tarigan Iskandar, Muhammad Irsyad Jovan Pangestu Juwita, Ayu Ratna Kiki Baihaqi Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M. Budi Kusarpoko M. Naufal Faqih Madyawati Latief Marsetio Marsetio Melia Siti Ajijah Miptahul Ulum Mochamad Djali Mohammad Djali Mohammad Djali Mohammad Djali Mohammad Djali Mudzakir, Tohirin Al Muhamad Amirrullah Muhammad Fadillah, Farhan Muhammad Ramadhan Mursyid Djawas Narwan Nahrudin Nina Puspitaloka Nofie Prasetiyo Nova Wulandari Praditya Putri Utami Pratama, Adi Rizky Pratiwi, Sinta Amanda Putra Rizki Pangestu Putri, Septiani Nuruldharma Rachmawati, Dhea Raden Duhita Diantiparamudita Utama Rahmat Rahmat Rahmat Rahmat Rahmat Restiana, Resti Ricky Steven Chandra Ridho Pratama, Ilham Ridwan, Ridwan Rizka Ayu Permana Rizki Ananda Rizki Nur Annisa Rizky Nugraha Rizky Riyanto Robi Andoyo Rohana, Tatang Rossi Indiarto Rusmin Saragih, Rusmin Sabirin Sandra Intan Sari Santi Lestari Seow, Eng Keng Siregar, Amril Siregar, Amril Mutoi Siregar, Amril Mutoi Siti Hanifah Khairun Nisa Suci Rahma Ajiaviaty Sukmawati, Cici Emilia Sulistya, Dwi Suningwar Mujiana Surya Martha Pratiwi Sutan Faisal Syahril, Ade Tatang Rohana Tita Rialita Tjong Wan Sen Tohirin Al Mudzakir Tsani Adiyanti Tukino, Tukino Wahiddin, Deden Wahyu Setio Aji Wazzan, Huda Wenda Adi Kusnaya Widiharto, Banani Yudo Devianto