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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Peternakan Integratif Elkom: Jurnal Elektronika dan Komputer Journal of Education and Learning (EduLearn) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization AdBispreneur Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informatika Aptisi Transactions on Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic Journal Sensi: Strategic of Education in Information System Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Bumigora Information Technology (BITe) Aiti: Jurnal Teknologi Informasi Infotech: Journal of Technology Information Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Applied Research (IJAR) Journal of Applied Data Sciences JOINTER : Journal of Informatics Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Information Technology (JIfoTech) Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Jurnal Algoritma Nusantara of Engineering (NOE) Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Blockchain Frontier Technology (BFRONT) Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Irwan Sembiring
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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IMPLEMENTASI KEAMANAN JARINGAN KOMPUTER DENGAN IPTABLES SEBAGAI FIREWALL MENGGUNAKAN PORT KNOCKING METODE DINAMIS Budi, Reza Setya; Sembiring, Irwan
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5750

Abstract

Keamanan jaringan adalah aspek penting dalam perlindungan data dan layanan dari ancaman siber. Salah satu metode inovatif yang digunakan untuk meningkatkan keamanan adalah dinamis port knocking (dynamic port knocking). Metode ini menggabungkan konsep dasar port knocking dengan elemen dinamis untuk memberikan lapisan perlindungan yang lebih kuat terhadap akses tidak sah. Port knocking tradisional melibatkan pengiriman serangkaian koneksi ke port tertutup dalam urutan tertentu untuk membuka akses ke layanan yang dilindungi. Namun, pendekatan ini dapat rentan terhadap serangan brute force dan replay. Dinamis port knocking memperbaiki kelemahan ini dengan mengubah urutan dan port yang harus diketuk berdasarkan parameter dinamis, seperti waktu atau informasi sesi yang dienkripsi. Dalam dinamis port knocking, pola knocking dapat berubah secara periodik atau berdasarkan algoritma tertentu, sehingga lebih sulit bagi penyerang untuk menebak urutan yang benar. Parameter dinamis dapat disesuaikan untuk menambah lapisan keamanan tambahan, seperti menggunakan token berbasis waktu atau informasi unik lainnya yang hanya diketahui oleh pengguna sah. Keuntungan utama dari dinamis port knocking meliputi peningkatan keamanan melalui perubahan urutan port secara berkala, mengurangi risiko deteksi oleh penyerang, dan meningkatkan kompleksitas serangan brute force dan replay. Selain itu, metode ini dapat diintegrasikan dengan protokol keamanan lain untuk membangun sistem pertahanan yang lebih komprehensif. Namun, implementasi dinamis port knocking juga memiliki tantangan, termasuk kebutuhan akan sinkronisasi waktu yang presisi antara klien dan server, serta kompleksitas dalam pengaturan dan pemeliharaan sistem. Dengan desain yang hati-hati dan pemanfaatan teknologi enkripsi yang kuat, dinamis port knocking dapat menjadi elemen penting dalam strategi keamanan jaringan modern, memastikan bahwa hanya pengguna yang berwenang dapat mengakses sumber daya yang dilindungi.
CRYPTO NARRATIVES SENTIMENT ANALYSIS ON BITCOIN PRICE PREDICTION USING THE NAIVE BAYES METHOD Nuryadi, Didik; Manongga, Daniel H.F.; Sembiring, Irwan
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6116

Abstract

Globalization affects many aspects of human life with consequences that may be positive or negative. Advances in information technology, which significantly assist many human activities, are one of the ele-ments affected. As a new product of financial technology, cryptocur-rency has revolutionized the global payment system. Bitcoin has expe-rienced significant price increases in recent years, often caused by eco-nomic and psychological market factors. Sentiment analysis of the bitcoin crypto narrative is essential for understanding market behavior and predicting price trends because market sentiment has been proven to influence bitcoin price movements. Therefore, this research aims to investigate the crypto sentiment narrative regarding Bitcoin price movements using a sentiment analysis approach with the Naïve Bayes classification method. The dataset used in this research comes from crypto narratives that are considered to influence bitcoin price move-ments, which were collected from October 2022 to April 2024. This re-search succeeded in classifying the data tested using 10-fold cross-validation testing, with an average of 76.13%. The precision score for the positive opinion class was 63.92%, and the precision score for the negative opinion class reached 81.77%. The average recall value for the positive class was 61.69%, and for the negative class, it reached 83.12%. This data shows that Naïve Bayes is quite good at analyzing crypto sentiment narratives regarding bitcoin price movements.
Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model Joko Siswanto; Irwan Sembiring; Adi Setiawan; Iwan Setyawan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20210

Abstract

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.
Peningkatan Knowledge Capture dan Knowledge Sharing dalam KMS Tools dengan Kaizen Form Faisal Hakim Amrullah; Hendry; Irwan Sembiring
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3409

Abstract

This study discusses the improvement of knowledge capture and knowledge sharing through the strengthening of a Kaizen-based Knowledge Management System (KMS) in the footwear manufacturing industry. The main problems include the suboptimal management of tacit knowledge and the limitations of document search based on simple keywords. This study applies an information retrieval method using TF-IDF and Cosine Similarity on 800 validated Kaizen documents through preprocessing, weighting, and document similarity measurement stages. The test results show that the proposed method performs better than conventional keyword-based search, with a precision value of 0.60, recall of 0.75, and F1-score of 0.67. The contribution of this study lies in the application of information retrieval methods to improve the effectiveness of knowledge retrieval in a Kaizen-based KMS, thereby supporting continuous improvement and organizational learning.
Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting Tri Wahyuningsih; Ade Iriani; Hindriyanto Dwi Purnomo; Irwan Sembiring
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p29-37

Abstract

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students. 
Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method Eka Purnama Harahap; Hindriyanto Dwi Purnomo; Ade Iriani; Irwan Sembiring; Tio Nurtino
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p19-28

Abstract

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.
Co-Authors Abas Sunarya, Po Ade Iriani Adi Setiawan Adriyanto Juliastomo Gundo Agus Sugiarto Agustinus, Ari Aji, Bintang Kristianto Andriana, Myra April Lia Hananto Apriliasari, Dwi Ardaneswari, Awanda Arthur, Christian Astawa, I Wayan Aswin Dew Ayu Sanjaya, Yulia Putri Bayu Setyanto Pamungkas Bayu, Teguh Indra Budhi Kristianto Budhi Kristianto Budi Santoso Budi, Reza Setya Cahyaningtyas, Christian Daniawan, Benny Danny Manongga Danny Sebastian Dedy Prasetya Kristiadi Dwi Hosanna Bangkalang Dwi Setiawan Edi Suharyadi Efendy, Rifan Eka Purnama Harahap Eko Sediono Eko Sediyono Eleazer Gottlieb Julio Sumampouw Elmanda, Vonda Erick Alfons Lisangan Esti Zakia Darojat Evangs Mailoa Evi Maria Faisal Hakim Amrullah Faturahman, Adam Fauzi Ahmad Muda Fian Yulio Santoso Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gerry Santos Lasatira Ginting, Jusia Amanda Girinzio, Iqbal Desam Gudiato, Candra Hamdan . Hasnudi . Henderi Henderi . Hendry Hendry, - Henuk, Yusuf Leonard Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Ignatius Agus Supriyono Ilham Hizbuloh Indrastanti Ratna Widiasari Iwan Setiawan Iwan Setiawan Iwan Setyawan Joko Listiawan Sukowati Joko Siswanto Jonas, Dendy Julians, Adhe Ronny Juneth Manuputty Krismiyati Kristoko D Hartomo Kristoko Dwi Hartomo Kusumajaya, Robby Andika Limbong, Josua Josen Alexander Manongga, Daniel H.F Manongga, Daniel H.F. Manongga, Daniel HF Marsyel Sampe Asang Marvelino, Matthew Mau, Stevanus Dwi Istiavan Maya Sari Merryana Lestari Migunani Migunani Mira Mira Mira Mohammad Ridwan Muhamad Yusup Nanle, Zeze Nazmun Nahar Khanom Nina Setiyawati Ninda Lutfiani Nining Fitriani Nugroho, Samuel Danny Nuryadi, Didik Nurzainah Ginting Pamungkas, Bayu Setyanto Phillnov Yohanes Pinontoan Pinontoan, Phillnov Yohanes Priatna , Wowon Purbaratri, Winny Purnomo, Hidriyanto Dwi Putra, Yonathan Rahadi Qurotul Aini Qurotul Aini R. Suharyadi Rahardja.,M.T.I.,MM, Dr. Ir. Untung Raymond Elias Mauboy Rimes Jopmorestho Malioy Roy Rudolf Huizen Saian, Septovan Dwi Suputra Sandry Lanovela Pasaribu Santoso, Nuke Puji Lestari Sediyono, Eko - Setiawan Hakim Sri Ngudi Wahyuni Sri Ngudi Wahyuni, Sri Ngudi Sri Yulianto Joko Prasetyo Suharyadi Sulistio Sulistio Sumampouw, Eleazer Gottlieb Julio Supriadi, Candra Susanti, Novita Dewi Sutarto Wijono Suwijo Danu Prasetyo Teguh Indra Bayu Teguh Wahyono Theopillus J. H. Wellem Tintien Koerniawati Tio Nurtino Tirsa Ninia Lina Tomasoa, Lyonly Tri Wahyuningsih Tri Wahyuningsih Tukino, Tukino Untung Rahardja Untung Rahardja Wibowo, Mars Caroline Wijaya, Angga Zakharia Wiwien Hadikurniawati Yerik Afrianto Singgalen Yessica Nataliani Yohan Maurits Indey Yohnes Madawara, Herdin Yulian Hany Makaruku