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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Information Management For Educators And Professionals (IMBI) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi METIK JURNAL Building of Informatics, Technology and Science Just TI (Jurnal Sains Terapan Teknologi Informasi) Brahmana : Jurnal Penerapan Kecerdasan Buatan Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Buletin Poltanesa Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Informatika Wicida eProceedings of Engineering J-Icon : Jurnal Komputer dan Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Sasambo: Jurnal Abdimas (Journal of Community Service)
Pitrasacha Adytia
Sistem Informasi, STMIK Widya Cipta Dharma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation Other

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Penguatan Literasi Digital Staff Pelayanan Publik di Kelurahan Sempaja Timur untuk Percepatan Transformasi Digital Sebagai Kota Penyangga IKN Wahyuni, Wahyuni; Adytia, Pitrasacha; Fahmi, Muhammad; Yunita, Yunita
Lumbung Inovasi: Jurnal Pengabdian kepada Masyarakat Vol. 8 No. 4 (2023): Desember
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/linov.v8i4.1494

Abstract

Untuk meningkatkan aktivitas pekerjaan dan akses layanan publik dalam berinternet serta mengatasi hambatan-hambatan yang dihadapi staff pelayanan publik di Kelurahan Sempaja Timur, maka ditawarkan suatu solusi yaitu dengan adanya penguatan literasi digital terhadap staff pelayanan publik di Kelurahan Sempaja Timur. Adapun metode pelaksanaan meliputi Survei Awal, Identifikasi potensi dan Peluang Mitra, Analisis Kebutuhan, Rencana Kegiatan Bersama, dan pelaksanaan kegiatan yang meliputi pra pelatihan, pelatihan dan pendampingan, serta pasca pelatihan. Kegiatan ini dilaksanakan di Kelurahan Sempaja Timur, Samarinda, Kalimantan Timur. Kegiatan dimulai dengan pengukuran indeks literasi digital kepada staff kelurahan berupa pre-test hasil dari pre-test di analisis kemudian dibuat modul dan pelatihan yang bersesuaian. Hasil dari pelatihan yang dilengkapi dengan modul LMS menunjukkan penguatan indeks literasi digital staff kelurahan, dimana pilar digital skill sub pilar kemampuan penggunaan teknologi sangat signifikan naik yaitu dari sebelumnya 2,71 (kurang) naik menjadi 3,21 (cukup). Dari kegiatan yang sudah dilakukan, dapat dilihat bahwa indeks literasi digital untuk masing-masing pilar mengalami peningkatan, khususnya pada pilar digital skill. Perlu adanya monitoring dan pendampingan untuk menumbuhkan semangat budaya digital di lingkungan kelurahan. Pelatihan diturunkan ke lingkup Rukun Tetangga yang berada di bawah Kelurahan Sempaja Timur. Dan pelatihan juga perlu disampaikan kepada masyarakat Kelurahan Sempaja Timur. Strengthening Digital Literacy of Public Service Staff in The East Sempaja District to Accelerate Digital Transformation as a City That Supports IKN To increase work activities and public service access to the internet as well as overcome the obstacles faced by public service staff in East Sempaja Village, a solution is offered, namely by strengthening digital literacy for public service staff in East Sempaja Village. Implementation methods include Initial Survey, identification of potential and Partner Opportunities, Needs Analysis, Joint Activity Plan, and implementation of activities including pre-training, training and mentoring, and post-training. This activity was carried out in East Sempaja Village, Samarinda, East Kalimantan. The activity began with measuring the digital literacy index for sub-district staff in the form of pre-test results from the pre-test which were analyzed and then appropriate modules and training were created. The results of the training equipped with the LMS module showed a strengthening of the digital literacy index of sub-district staff, where the digital skills pillar, sub-pillar, ability to use technology, increased very significantly, namely from the previous 2.71 (poor) to 3.21 (sufficient). From the activities that have been carried out, it can be seen that the digital literacy index for each pillar has increased, especially for the digital skills pillar. There is a need for monitoring and assistance to foster the spirit of digital culture in the sub-district environment. The training was expanded to cover the Neighborhood Units under the East Sempaja Village. And training also needs to be delivered to the people of East Sempaja Village.
Identifikasi Serangan DDOS Pada Jaringan Komputer Menggunakan Algoritma Artificial Neural Network Kelik Sussolaikah; Pitrasacha Adytia; Wahyuni Wahyuni; Lisda Aulia Rahmi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 1 No. 1 (2023): Februari : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v1i1.67

Abstract

DDOS (Distribute Denial of Service) is a type of structured attack. This attack has been around since 1990. DDoS attacks are capable of paralyzing servers by flooding network traffic and causing it to go down. To overcome this problem, the way to detect DDoS attacks has several methods and algorithms, one of which is the Artificial Neural Network algorithm and uses the Machine learning method due to the fast computing process, high accuracy, and this research uses the SKKNI research method Number 299 of 2020. The analysis was carried out uses training data from the latest dataset, namely CICIDS2017, which is a development of a previously existing dataset. DDoS attack detection testing using the confusion matrix method obtained bot precision of 0.99, recall of 0.99, and f1score of 0.99, 3
Implementasi Sistem Informasi LPM Sempaja Timur Sebagai Percepatan Peningkatan Kesejahteraan Masyarakat Pitrasacha Adytia; Wahyuni Wahyuni; Rizky Zakariyya Rasyad; Muhammad Fahmi
Sasambo: Jurnal Abdimas (Journal of Community Service) Vol. 6 No. 4 (2024): November
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/sasambo.v6i4.2213

Abstract

Penelitian ini bertujuan mengimplementasikan Sistem Informasi Lembaga Pemberdayaan Masyarakat (SILPM) di Kelurahan Sempaja Timur untuk meningkatkan kesejahteraan masyarakat melalui digitalisasi manajemen data, pemantauan kegiatan, dan kolaborasi. Mitra utama adalah LPMK Sempaja Timur, yang berperan aktif dalam perencanaan dan pelaksanaan program. Metode pelaksanaan menggunakan pendekatan partisipatif melalui sosialisasi, pelatihan, penerapan teknologi, serta pendampingan dan evaluasi. Hasil menunjukkan peningkatan signifikan, dengan 100% anggota LPM yang dilatih mampu mengoperasikan sistem informasi, serta 90% responden merasa terbantu dalam mengakses informasi dan pelaporan melalui SILPM. Sistem ini berhasil mendigitalisasi 95% data anggota dan mencatat 80% laporan kegiatan secara real-time. Kesimpulannya, digitalisasi manajemen LPM mendukung peningkatan transparansi, efisiensi program, dan keterlibatan masyarakat. Rekomendasi mencakup pelatihan lanjutan, pemantauan keberlanjutan sistem, dan integrasi dengan program pemberdayaan lainnya. Implementation of the East Sempaja LPM Information System as an Acceleration of Increasing Community Welfare This study aims to implement the Community Empowerment Institution Information System (SILPM) in East Sempaja Village to enhance community welfare through digitalized data management, activity monitoring, and collaboration. The main partner is the East Sempaja Community Empowerment Institution (LPMK), actively involved in planning and execution. The participatory method included socialization, training, technology application, and monitoring and evaluation. The results demonstrate significant improvements, with 100% of trained LPM members able to operate the information system and 90% of respondents finding it beneficial for accessing information and reporting. The system successfully digitalized 95% of member data and recorded 80% of activity reports in real-time. In conclusion, LPM management digitalization supports enhanced transparency, program efficiency, and community engagement. Recommendations include advanced training, system sustainability monitoring, and integration with other empowerment programs.
Pengembangan Sistem Deteksi Hand Gesture untuk Mempermudah Menghafal Sandi Morse dengan Metode KNN Wahyuni, Wahyuni; Pitrasacha Adytia; Adha Trisna Lidya
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2202

Abstract

Sandi morse adalah teknik komunikasi unik yang masih digunakan dalam berbagai konteks, seperti komunikasi darurat dan amatir radio. Pengendali frekuensi radio di Indonesia sering menghadapi kesulitan dalam menghafal sandi morse. Media pembelajaran sandi morse saat ini masih terbatas pada titik dan garis yang sulit untuk dihafalkan. Penelitian ini mengembangkan sistem deteksi hand gesture menggunakan metode K-Nearest Neighbors (KNN) untuk mempermudah penghafalan sandi morse. Sistem ini memanfaatkan gerakan tangan seperti mengepal dan membuka telapak tangan, untuk mewakili kombinasi titik dan garis dalam sandi morse, dengan harapan membuat proses belajar lebih intuitif dan interaktif. Implementasi sistem dilakukan dengan menggunakan webcam, algoritma Mediapipe, library OpenCV, dan aplikasi Unity. Kemudian model dievaluasi performanya dan serta antarmukanya diuji degan blackbox. Sistem deteksi hand gesture berhasil mengidentifikasi huruf abjad berdasarkan gerakan tangan dengan akurasi minimal 60%. Pengujian lebih lanjut menggunakan KNN dengan nilai K-1, menunjukkan rata-rata akurasi sebesar 81%. Sehinga sistem efektif dalam mendeteksi gerakan tangan untuk mempermudah penghafalan sandi morse. Secara keseluruhan, dengan akurasi rata-rata 81%, sistem deteksi hand gesture ini menunjukkan potensi besar dalam meningkatkan pembelajaran sandi morse secara efektif dan menarik. Kendala utama dalam penelitian ini adalah terbatasnya data partisipan, yang mengakibatkan variasi dalam gerakan tangan dan potensi tumpang tindih antara kelas gerakan. Penelitian ini membutuhkan lebih banyak data untuk meningkatkan akurasi dan mengurangi kesalahan dalam deteksi gerakan. Sehingga, pada penelitian selanjutnya diharapkan peneliti memperbanyak dataset yang digunakan pada deteksi gerakan tangan untuk sandi morse.
Pengembangan Chatbot Berbasis AI untuk Mendukung Pelayanan Perpustakaan Muhammad Ahsanu Qaulan; Wahyuni; Pitrasacha Adytia
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2283

Abstract

Penelitian ini mengembangkan chatbot berbasis kecerdasan buatan (AI) untuk mendukung layanan informasi perpustakaan di STMIK Widya Cipta Dharma. Pengembangan chatbot dilakukan dengan pendekatan CRISP-DM dan teknologi LLM (Llama3.2) yang diintegrasikan melalui metode Retrieval-Augmented Generation. Dataset yang digunakan terdiri dari 11 pasangan pertanyaan-jawaban, kemudian dilakukan proses preprocessing, embedding vektor, dan pencarian dokumen menggunakan FAISS. Evaluasi dilakukan menggunakan metrik BERTScore untuk mengukur kesamaan semantik antara jawaban chatbot dan referensi, dengan hasil rata-rata precision sebesar 0.6513, recall sebesar 0.7924, dan F1-Score sebesar 0.7124. Nilai tersebut menunjukkan bahwa chatbot memiliki kemampuan semantik yang baik dalam menjawab pertanyaan umum terkait layanan perpustakaan, meskipun masih memerlukan pengembangan lebih lanjut untuk meningkatkan akurasi pada pertanyaan yang kompleks.
Modeling The Prediction of Hard Drive Capacity Usage on Server Computers Based on Linear Regression Wahyuni, Wahyuni; Adytia, Pitrasacha; Astin, Siti Namira Rizqi; Sussolaikah, Kelik; Kasim, Fadly
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28851

Abstract

Bank of XYZ has a server computer that is used to run several information technology application services such as ATMs and others. Because the server computer uses a hard drive, the full hard drive can cause problems with the service not operating properly. Full hard drives occur without being noticed. So that this makes the computer server problematic, resulting in customer dissatisfaction and decreased customer loyalty to Bank XYZ. To solve the problem at XYZ Bank, one of the machine learning algorithms can be used to predict hard drive capacity. The method used to predict hard drive storage or usage. The machine learning algorithm used is Multiple Linear Regression. The results of this study show that the linear regression model successfully predicts the use of hard drive capacity on server computers with a sufficient level of accuracy.But it is still not optimal because only a few servers can be predicted. For further research, may consider using the LSTM (Long Short-Term Memory) algorithm. LSTM is an algorithm that is well-suited for sequence prediction problems, including time series forecasting.
Coffee Type Classification Using Backpropagation Artificial Neural Network Adytia, Pitrasacha; Wahyuni, Wahyuni; Asmaramany, Dimas; Sussolaikah, Kelik
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28853

Abstract

Coffee has several types including robusta coffee, arabica coffee and luwak coffee. Each coffee has certain characteristics of color, texture, aroma and also the quality of the taste. Coffee counterfeiting is also common. This coffee counterfeiting usually uses materials such as corn, wheat, soybeans, husks, sticks and robusta coffee beans. So that a model is needed to be able to classify the type of coffee. This research uses artificial neural network machine learning algorithms to identify and classify coffee. Quality training and testing data is needed in this method because it will affect the final results. Initial data is collected via e-nose, with this equipment data on changes in electrical voltage will be obtained from 4 sensors, namely MQ-2, MQ-3, MQ-7 and MQ-135. These 4 features will be used in the classification process. With 900 sets of training data, the test results show that the neural network is able to provide correct classification 99% of the 3 sets of testing data. The results of training and testing show that the neural network formed can identify and distinguish coffee types with good results.
Development Geographic Information System for Forest Mapping in Kutai Kartanegara Regency Salmon, Salmon; Adytia, Pitrasacha; Niansyah, Sugih; Andriawan; Andrea, Reza
TEPIAN Vol. 4 No. 3 (2023): September 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v4i3.2890

Abstract

The agricultural, plantation, and forestry industries that are the main choice of the population to meet household food needs and boost the community's economy are very much in line with the geographical contours of the Regency Kutai Kartanegara. A geographic information system that can provide information on position, location coordinates, forest areas, forest information in Kutai Kartanegara Regency, and paths to find the location of forest areas. A web- based Geographic Information System (GIS) is required to determine the current position and location of the forest. The waterfall method is used to build this GIS framework, which involves stages such as analysis, design, code generation, testing, and maintenance. MySQL is a database management system. PHP, JavaScript, and HTML are used to create programming languages. Bootstrap user interface implementation. Black box testing is used to verify the software. The test results show that the GIS created meets the requirements and can resolve system issues.
Identifikasi Serangan Low-Rate DDOS Berbasis Deep Learning Wahyuni, Wahyuni; Adytia, Pitrasacha
Poltanesa Vol 23 No 2 (2022): Desember 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i2.1737

Abstract

LowRate DDoS (LDDoS) is a variation of DDoS attack that sends fewer packets than conventional DDoS attacks. However, by sending a smaller number of packets and using a unique attack period, low-rate DDoS is very effective in reducing the quality of an internet network-based service due to full access. On the other hand, the low-rate DDoS with its nature also makes it difficult to detect because it looks more mixed with normal user access. The Deep Learning model that will be used in this research is the RNN LSTM (Long Short Term Memory) model. LSTM is a neural network architecture which is good enough to process sequential data. This model is better than the simple RNN model. The research method is adapted to the SKKNI No. 299 of 2020. However, this research will be carried out until the model development stage, namely the evaluation model. From the results of the research that has been done, it can be concluded that the RNN LSTM model can be used to classify low-rate DDOS attacks using feature selection. The accuracy of the training data on the validation data is around 98% and after visualizing the data for accuracy and loss, it can be concluded that the model is quite good, aka there is no underfitting or overfitting. While the accuracy obtained for testing data is 0.97%.
Penerapan Algoritma Support Vector Machine (SVM) dalam Analisis Sentimen Mahasiswa Terhadap Sistem Layanan KPST STMIK Widya Cipta Dharma Maulana Umar; Pitrasacha Adytia; Amelia Yusnita
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9189

Abstract

Abstrak - Sistem Layanan KPST di STMIK Widya Cipta Dharma menghadapi tantangan disparitas kepuasan mahasiswa, terutama dalam aspek administratif dan bimbingan akademik. Tujuan: Penelitian ini bertujuan untuk menganalisis sentimen mahasiswa secara komprehensif menggunakan algoritma Support Vector Machine (SVM) dengan dataset aktual, mengoptimasi hyperparameter, dan mengatasi ketidakseimbangan data. Metode: Data dikumpulkan melalui survei terstruktur (N=150 responden), diproses dengan TF-IDF (ngram_range=(1,3), max_features=1500), dan diklasifikasi menggunakan SVM dengan teknik GridSearchCV untuk optimasi parameter (C=10, gamma=0.01, kernel=RBF). SMOTE diterapkan untuk menangani ketidakseimbangan kelas. Hasil: Model mencapai akurasi 82.3% dengan presisi 80.1% pada kelas minoritas (negatif). Analisis mengungkap sentimen positif dominan (68%) pada kecepatan respons admin (skor 4.0), namun isu kritis teridentifikasi di antarmuka pengguna (32% komentar negatif) dan kualitas bimbingan (skor 3.5). Kesimpulan: Penelitian ini membuktikan efektivitas SVM dalam analisis sentimen akademik, dengan rekomendasi spesifik untuk redesain UI/UX dan integrasi sistem notifikasi otomatis. Temuan juga menyoroti pentingnya penanganan ketidakseimbangan data dalam klasifikasi teks.Kata kunci: Analisis Sentimen; Support Vector Machine (SVM); Sistem Layanan KPST; Ketidakseimbangan Data; Optimasi Hyperparameter; UI/UX Akademik.                            Abstract - The KPST Service System at STMIK Widya Cipta Dharma faces challenges in student satisfaction disparities, particularly in administrative and academic guidance aspects. Objective: This study aims to comprehensively analyze student sentiment using the Support Vector Machine (SVM) algorithm with real-world datasets, optimizing hyperparameters and addressing data imbalance. Method: Data was collected through structured surveys (N=150 respondents), processed with TF-IDF (ngram_range=(1,3), max_features=1500), and classified using SVM with GridSearchCV for parameter optimization (C=10, gamma=0.01, kernel=RBF). SMOTE was applied to handle class imbalance. Results: The model achieved 82.3% accuracy with 80.1% precision for the minority class (negative). Analysis revealed dominant positive sentiment (68%) on administrative response (score 4.0), but critical issues were identified in user interface (32% negative feedback) and academic guidance quality (score 3.5). Conclusion: This research demonstrates SVM's effectiveness in academic sentiment analysis, with specific recommendations for UI/UX redesign and automated notification system integration. Findings also highlight the importance of addressing data imbalance in text classification.Keywords: Sentiment Analysis; Support Vector Machine (SVM); KPST Service System; Data Imbalance; Hyperparameter Tuning; Academic UI/UX.
Co-Authors Adha Trisna Lidya Alvin Hernanda Ekaputra Amelia Yusnita Andriawan Arriyanti, Eka Asmaramany, Dimas Astin, Siti Namira Rizqi Aulia Abdul Rahman Didi Kuswandi Ekawati Yulsilviana Ekawati, Hanifah Fariz Mirza Ahmad Hanifah Ekawati Heny Pratiwi Heny Pratiwi I Nengah Riandika Ika Nurmila Avinda Ita Arfyanti Kasim, Fadly Kelik Sussolaikah Lisda Aulia Rahmi Maulana Umar Muhammad Ahsanu Qaulan Muhammad Fachri Sanjaya Muhammad Fahmi Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Niansyah, Sugih Nurhuda, Asep Nursobah, Nursobah Rahmat Hidayat Reza Andrea Rizky Zakariyya Rasyad Salmon Salmon Salmon Salmon, Salmon Solly Aryza Supriady Supriady Sussolaikah, Kelik Tommy Bustomi Ujang Sumarwan Wahyuni - Wahyuni wahyuni wahyuni Yudha Satria Yuli Adam Prasetyo Yunita Yunita Yunita Yunita