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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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Sentiment Analysis on the Relocation of the National Capital (IKN) on Social Media X Using Naive Bayes and K-Nearest Neighbor (KNN) Methods Wulandari, Nova; Cahyana, Yana; Rahmat, Rahmat; Hikmayanti, Hanny
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.v9i3.9552

Abstract

This study investigates public sentiment toward the relocation of Indonesia’s capital from Jakarta to East Kalimantan, focusing on reactions from social media platforms such as X (formerly Twitter). Understanding these sentiments is crucial for the government to gauge support for this significant policy shift. The study compares the performance of two classification algorithms, Naïve Bayes and K-Nearest Neighbor (K-NN), in sentiment analysis. A total of 1.277 comments were collected using the tweet-harvest library through a crawling process. The data underwent preprocessing, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming. Sentiment labels were assigned through both manual and automated methods, while feature extraction was performed using the TF-IDF technique. The algorithms' performance was assessed using accuracy, precision, recall, and F1-score metrics. The results revealed that Naïve Bayes outperformed K-NN, with an accuracy of 70%, precision of 72%, recall of 70%, and an F1-score of 69%. In contrast, K-NN achieved an accuracy of 60%, precision of 62%, recall of 60%, and an F1-score of 59%. These results suggest that Naïve Bayes is more effective in classifying sentiment related to the capital relocation. The findings offer valuable insights for policymakers and highlight the potential of automated sentiment analysis as a tool for monitoring public opinion on major governmental policies.
Comparison of K-Nearest Neighbors and Naive Bayes Classifier Algorithms in Sentiment Analysis of 2024 Election in Twitter (X) Enjelia, Lola; Cahyana, Yana; Rahmat; Wahiddin, Deden
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.v9i3.9593

Abstract

This study compares the performance of the K-Nearest Neighbors (K-NN) and Naive Bayes Classifier (NBC) algorithms in sentiment analysis of the 2024 Regional Election (Pilkada) based on Indonesian local data sourced from platform X. A total of 1,187 tweets were collected through crawling, followed by extensive preprocessing and manual sentiment labeling by a professional linguist to ensure data validity and reliability. The study highlights NBC's superior accuracy (81.05%) compared to K-NN (75.26%), largely due to the characteristics of short-text social media data that align with NBC's independence assumptions. Key terms identified through TF-IDF analysis include “pilkada”, “2024”, and “damai” in positive sentiment, while “mahkamah konstitusi” and “kalah” dominated negative sentiment. The results imply that although public discourse largely supports the election process, critical sentiments toward election dispute issues persist. These findings offer practical implications for election authorities, policymakers, and digital campaign strategists, particularly in optimizing public communication strategies, early detection of potential conflicts, and designing public opinion monitoring systems based on real-time sentiment analysis. By leveraging high-quality labeled local data, this study makes a significant contribution to modeling public opinion dynamics in Indonesia during political events.
PELATIHAN HAK ASASI DIGITAL UNTUK MENINGKATKAN PERLINDUNGAN KEAMANAN DI DUNIA MAYA Utami, Praditya Putri; Ambarwati, Evi Karlina; Dewi, Indah Purnama; Cahyana, Yana; Hanan, Sofiah Marwah; Putri, Septiani Nuruldharma
Jurnal Abdi Insani Vol 12 No 3 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i3.2460

Abstract

Perkembangan Teknologi Informasi Komunikasi (TIK) dalam dunia pendidikan memiliki peranan yang penting. Salah satu amanat Undang-undang Nomor 14 Tahun 2005 tentang Dosen dan Guru adalah untuk memiliki kompetensi sesuai dengan perkembangan zaman, termasuk menguasai TIK. Guru harus memahami hak asasi digital seiring dengan kemajuan teknologi pembelajaran dan prioritas pemerintah untuk program digitalisasi pendidikan. Pengabdian kepada Masyarakat ini bertujuan untuk mendukung para guru RA At-Taqwa Kelapadua Tanjungbungin di Kecamatan Pakisjaya, Kabupaten Karawang menguasai TIK dalam pembelajaran. Kegiatan dilakukan dengan memberikan pelatihan serta demontrasi tentang hak asasi digital dan keamanan siber. Program pengabdian masyarakat menggunakan metode Experiential Learning melalui demonstrasi untuk meningkatkan pengetahuan guru tentang hak asasi digital dan keamanan siber. Kegiatan terdiri dari tiga tahap: persiapan, pelaksanaan (sosialisasi, pembelajaran, demonstrasi), dan evaluasi menggunakan kuesioner skala Likert. Kegiatan PkM di RA At-Taqwa Kelapadua, Karawang, diikuti 45 guru RA. Pelatihan meningkatkan pemahaman guru tentang hak asasi digital dan keamanan siber. Evaluasi menunjukkan guru memiliki kompetensi digital kategori "baik," terutama dalam kesadaran risiko dan perlindungan data pribadi. Namun, aspek terendah adalah membagikan informasi pribadi secara aman. Diskusi interaktif membantu guru mengenali ancaman dunia maya dan membimbing siswa. Hasil ini sejalan dengan penelitian global tentang pentingnya kesadaran digital dalam pendidikan. Oleh Karena itu, pelatihan Hak Asasi Digital berhasil meningkatkan pemahaman guru dan menekankan pentingnya kolaborasi dalam meningkatkan literasi digital.
PENGARUH PENAMBAHAN PATI SAGU TER-PREGELATINISASI TERHADAP RETENSIFE DAN ZN PADA KERNEL BERAS TER-FORTIFIKASI YANG DIBUATDARI BERAS IR64 DAN IR42 Abdullah Darussalam; Yana Cahyana; Herlina Marta; M. Budi Kusarpoko; Sabirin
Jurnal Sains dan Teknologi Pangan Vol. 10 No. 4 (2025): Jurnal Sains dan Teknologi Pangan
Publisher : Jurusan Ilmu dan Teknologi Pangan Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63071/f05g6f58

Abstract

Indonesia mencatat angka prevalensi stunting sebesar 21,5% pada tahun 2023. World Food Programme (WFP) merekomendasikan Fortified Rice Kernels (FRK) sebagai sumber mineral esensial yang andal, khususnya zat besi (Fe) dan seng (Zn), bagi perempuan dan anak-anak. Namun, mineral-mineral tersebut sering hilang selama proses pembilasan beras. Meskipun Indonesia memiliki sumber daya sagu yang melimpah, pemanfaatannya dalam sektor pangan masih terbatas, sehingga diperlukan strategi inovatif untuk peningkatan nilai tambah dan diversifikasi produk.Penelitian ini mengkaji pengaruh penambahan Pati Sagu Terpregelatinisasi (PST) dalam formulasi FRK. PST diproduksi melalui proses ekstrusi pada suhu 60–90 °C dan kecepatan 80 rpm, dengan tiga tingkat kadar air berbeda: 25% (PST25), 35% (PST35), dan 45% (PST45). PST kering kemudian dicampur dengan tepung beras IR42 (amilosa 32,85%) dan IR64 (amilosa 27,93%), masing-masing dengan penambahan premiks mikronutrien sebesar 7%. FRK diproduksi melalui ekstrusi pada suhu 50–85 °C dan kecepatan 31 rpm. Retensi Fe tertinggi (91,1%) diperoleh pada FRK yang dibuat dari campuran IR64 dan PST45 dengan rasio 4:1, sedangkan retensi Zn tertinggi (99,4%) ditemukan pada campuran IR42 dan PST35 dengan rasio yang sama. Selain itu, campuran IR64 dan PST35 dengan rasio 3:1 menghasilkan retensi Zn sebesar 98,2%. Temuan ini menunjukkan bahwa beras dengan kadar amilosa tinggi (IR42) paling optimal dikombinasikan dengan PST35, sedangkan beras dengan kadar amilosa lebih rendah (IR64) memberikan hasil terbaik jika dikombinasikan dengan PST45 dalam meningkatkan retensi Fe dan Zn. Analisis tambahan juga dilakukan terhadap atribut warna, derajat penyerapan air, dan tingkat gelatinisasi, yang merupakan parameter penting dalam evaluasi mutu beras analog.
Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree Pratiwi, Sinta Amanda; Fauzi, Ahmad; Puspita Lestari, Santi Arum; Cahyana, Yana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1681

Abstract

A pharmacy is a place for buying and selling drugs and must have an adequate stock of drug supplies so that it can serve consumers in need. In some pharmacies there are problems related to drug supply. Often the drugs needed by the community are empty in stock, while the drugs that are less needed are stored in the warehouse. Therefore, this study aims to conduct a prediction model of drug supply so that it can meet consumer needs. This study uses drug inventory data at Kaligandu Pharmacy, the data has 2745 rows and 5 attributes consisting of "Item Name", "Unit", "Previous Stock", "Rill Stock", and "Restock". The method used in this case is the Decision Tree algorithm with Accuracy, Precision, Recall, and F1-Score evaluation methods to see which drugs are available and not available based on "Unit". The results showed that the Decision Tree algorithm obtained good results by using a data comparison of 80 to 20 resulting in an accuracy value of 98.71%. In addition, the resulting values of Precision, Recall, and F1-Score are not much different, namely 0.9872, 0.9872, and 0.9867. The 70 to 30 data comparison produces a smaller value but is not much different from the results of 80 to 20, namely the accuracy of 98.28%, Precision 0.9832, Recall 0.9828, and F1-Score 0.9804. with these results this research can be continued by implementing drug inventory prediction using Decision Tree into an application
PERSEPSI ORANGTUA TERHADAP FENOMENA PENGGUNAAN GADGET PADA ANAK-ANAK DI DESA TALUNAJAYA Yana Cahyana
JURNAL BUANA PENGABDIAN Vol. 5 No. 2 (2023): JURNAL BUANA PENGABDIAN
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/jurnalbuanapengabdian.v5i2.5789

Abstract

Dalam era modernisasi yang sedang berlangsung saat ini, banyak hal yang terjadi dalam kehidupan manusia, termasuk pada bidang teknologi dan sosial budaya. Teknologi dapat digunakan secara luas sehingga menyebabkan pergeseran tatanan masyarakat bahkan masalah lingkungan. Oleh karena itu di lakukan penelitian yang ini yang berjudul persepsi orangtua terhadap fenomena penggunaan gadget pada anak-anak di desa Talunajaya Dengan bertujuan unutk mengedukasi orang tua untuk lebih memperhatikan anak-anak dari pengaruh gadget dalam kehidupan sehari hari. Penelitian ini menggunakan metode kualitatif melibatkan pendekatan Studi Kasus dengan menggunakan Teknik pengumpulan data melalui wawancara. Lanjutan
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Siregar, Amril Mutoi; Hartono Wijaya, Sony; Fauzi, Ahmad; Sen, Tjong Wan; Faisal, Sutan; Tukino, Tukino; Cahyana, Yana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
CLASSIFICATION OF RICE PLANTS AFFECTED BY RATS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM Nofie Prasetiyo; Baihaqi, Kiki Ahmad; Lestari, Santi Arum Puspita; Cahyana, Yana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1949

Abstract

In the era of Indonesia's agrarian economy which is supported by the agricultural sector, rice plants play an important role in meeting food needs. However, pest attacks, especially field mice, can cause significant losses in rice production. To overcome this, this research proposes the use of the Support Vector Machine (SVM) algorithm with the Particle Swarm Optimization method in predicting rat pest attacks on rice plants. This research involves the process of collecting data from drone photos to identify affected agricultural land. The preprocessing stage involves changing colors from RGB to GRAY and zoom augmentation. Feature extraction is carried out using Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Testing was carried out involving the SVM/SVC model and performance evaluation was carried out using accuracy, precision and recall metrics. The preprocessing test results showed an increase in performance with training accuracy of 68.33%. However, the actual prediction on the original image results in a low accuracy of around 25%. However, image testing after involving the entire process, including preprocessing and model prediction, shows a higher level of accuracy, reaching around 90%.
IMPLEMENTATION OF THE YOLOV8 METHOD TO DETECT WORK SAFETY HELMETS Direja, Azhar Ferbista; Cahyana, Yana; Rahmat, Rahmat; Baihaqi, Kiki Ahmad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2005

Abstract

Work safety helmets are an important tool in OHS (Occupational Health and Safety) that must be used by workers. Workers who work with heavy equipment must wear work safety helmets as an obligation. Unfortunately, there are still many workers who do not comply with this rule. They will only wear helmets if there is supervision from a supervisor. However, if the supervisor is not on site, many workers will remove their helmets. The need for supervision of workers is important in reducing work accidents. From these problems, a work safety helmet detection model was created using the YOLOv8 method. This implementation aims to increase the accuracy values ​​obtained and can reduce workload and increase efficiency in checking violations of the use of work safety helmets among workers. The method used consists of several stages, namely image acquisition of 670 images, image labeling, preprocessing, augmentation in roboflow, YOLOv8x model training with 100 epochs, image testing with a distance of 1, 3, 5 meters between the object and the camera, evaluation of test results. Based on the results of training with 467 images, the mAP50 reached 99.5%. Meanwhile, the test results with 100 images showed an accuracy of 99%.
Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Terhadap Ulasan Pengguna Aplikasi Mypertamina Menggunakan Confusion Matrix Syahril, Ade; Cahyana, Yana; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5639

Abstract

The large number of vehicles in Indonesia makes fuel oil (BBM) very important, especially for cars and motorbikes. The Indonesian government works closely with PT Pertamina Persero and requires transactions using the MyPertamina application to ensure that fuel subsidies are properly targeted. However, the MyPertamina app has received mixed feedback and criticism from users, such as complaints about frequent bugs, instability of the app during use and difficulties in the registration or login process. User feedback on the app has been both positive and negative. Users also provided their ratings and reviews on the Google Play Store. The purpose of this research is to analyse the opinions of MyPertamina application user comments and compare the accuracy of the Decision Tree and K-Nearest Neighbor algorithms. This research includes scraping, text preprocessing, weighting, algorithm implementation and evaluation. The data used was obtained from Google Play Store as much as 10,000 data based on the latest reviews, after data cleaning such as removing duplicate data and missing values obtained 8,072 reviews. The data is then grouped into positive classes (2,506 reviews) and negative classes (5,566 reviews), with more negative data. The classification results using the Decision Tree and K-NN methods, it is known that the Decision Tree method has a higher accuracy of 83%, while K-NN method is 58%. This finding indicates that the Decision Tree method is more effective in analysing user reviews of the MyPertamina application compared to the K-NN method.
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