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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Rekam : Jurnal, Fotografi, Televisi Animasi SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Bioedukasi JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Sains Dan Teknologi (SAINTEKBU) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Economic, Management, Accounting and Technology (JEMATech) KOMPUTIKA - Jurnal Sistem Komputer Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Bitnet: Jurnal Pendidikan Teknologi Informasi EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Building of Informatics, Technology and Science Gema Wiralodra Dinasti International Journal of Education Management and Social Science Jurnal Tecnoscienza Generation Journal Jurnal Mnemonic Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics PRAJA: Jurnal Ilmiah Pemerintahan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) JINAV: Journal of Information and Visualization International Journal of Artificial Intelligence and Robotics (IJAIR) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknologi Komputer ( J-ICOM) DEVICE Djtechno: Jurnal Teknologi Informasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer JURNAL STUDIA KOMUNIKA Jurnal Pengabdian Seni KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Computer Science and Informatic Systems : J-Cosys Jurnal Mandiri IT Sulawesi Tenggara Educational Journal JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Jurnal Sisfotek Global International Journal Artificial Intelligent and Informatics Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Innovation Research and Knowledge Malcom: Indonesian Journal of Machine Learning and Computer Science Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Scientific Journal of Informatics Pengabdian Seni Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal TAM (Technology Acceptance Model) Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika)
Kusrini, Kusrini
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OPTIMASI HYPERPARAMETER MODEL LSTM DAN VARIANNYA UNTUK PERAMALAN PEMBELIAN BAHAN BAKU KARET ALAM Andika, Roy; Kusrini, Kusrini
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7567

Abstract

Penelitian ini mengkaji optimasi hyperparameter pada model peramalan deret waktu untuk memprediksi pembelian bahan baku karet alam. Tiga arsitektur model—LSTM, Bi-LSTM, dan Stacked LSTM—dieksekusi dengan menerapkan tiga metode tuning, yaitu Bayesian Optimization, Hyperband, dan Optuna. Proses tuning dilakukan dengan mengeksplorasi berbagai kombinasi parameter, seperti jumlah epoch, units, dropout rate, learning rate, batch size, dan units2, dengan model dikompilasi menggunakan fungsi loss MSE dan metrik MAE. Hasil penelitian menunjukkan bahwa learning rate dan dropout rate memiliki pengaruh signifikan terhadap penurunan error, sedangkan peningkatan units2 dapat meningkatkan risiko overfitting jika tidak diimbangi dengan strategi regularisasi yang tepat. Analisis mendalam mengungkap bahwa kombinasi Bayesian dengan Stacked LSTM menghasilkan performa terbaik pada subset data dengan score terendah, sedangkan Optuna menunjukkan konsistensi optimal untuk model LSTM. Menariknya, model Bi-LSTM tidak mencapai konfigurasi optimal, kemungkinan disebabkan oleh sensitivitas tuning yang lebih tinggi atau kompleksitas arsitektur yang tidak sesuai dengan karakteristik dataset yang digunakan. Temuan ini memberikan wawasan penting untuk pengembangan model peramalan yang lebih akurat dan efisien serta membuka peluang penelitian lanjutan dalam strategi optimasi hyperparameter yang adaptif dan robust.
Photography Strategies in the Challenges of Industry 4.0 and Society 5.0 Samaratungga, Oscar; Kusrini, Kusrini
Jurnal Multidisiplin Sahombu Vol. 5 No. 06 (2025): Jurnal Multidisiplin Sahombu, September - October (2025)
Publisher : Sean Institute

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

Abstract

Photography faced significant challenges during the COVID-19 pandemic due to restrictions on space for in-person meetings, while previous photo shoots required in-person meetings. During this period, technological and industrial developments began to be introduced in the era of Industry 4.0 and Society 5.0, but their popularity was overshadowed by information about COVID-19. This paper aims to determine the strategies of photographers in facing the challenges of Industry 4.0 and Society 5.0. The method used is qualitative, with data collection through archival/document studies and literature studies. Albert Joseph Toynbee's challenge and response theory is used to discuss these issues. COVID-19 has also influenced photography in finding strategies for professional photography practice. One of these is virtual photoshoots or remote photography. The challenges of photography have become increasingly complex with the emergence of Artificial Intelligence (AI). Photographers are also facing new challenges. This condition is addressed by considering the convenience offered by AI, namely, combining AI results with photography for photo functions used for promotions or business
Evaluation of E-Learning Usability Based on ISO 25010 with Hofstede's Cultural Dimensions as Moderation: A PLS-SEM Study in Higher Education Januhari, Ni Nyoman Utami; Setyanto, Arief; Kusrini, Kusrini; Utami, Ema; Béjar, Rodrigo Martínez
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45738

Abstract

Although e-learning has rapidly advanced in higher education, many platforms still fall short of meeting user needs due to a lack of integration between usability and cultural dimensions. This study explores how usability influences user satisfaction with e-learning platforms, with cultural dimensions based on Hofstede’s model examined as moderating variables. Usability Quality (QiU) is assessed using the ISO/IEC 25010 framework, which includes five key elements: effectiveness, efficiency, user satisfaction, risk avoidance, and contextual relevance. A total of 384 students from private universities in Bali participated in the study, representing a diverse range of academic disciplines. Using SmartPLS and Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis revealed that usability has a significant effect on user satisfaction (T=7.528, β=0.270), and cultural variables also play a substantial role (T=21.094, β=0.704). Although the moderating effect of culture was statistically significant (T=2.379, β=0.042), its impact was relatively modest compared to the direct effect of usability. Among the usability components, efficiency emerged as the most influential factor. Regarding cultural dimensions, individualism versus collectivism was found to have the strongest effect. These findings emphasize the importance of designing e-learning systems that are both usability-driven and culturally sensitive, ensuring alignment with user expectations and the educational context.  
Analisis Perbandingan Kinerja Web Humas Infrastruktur On-Premise dan Cloud Computing dengan Load Balancer Round Robin: Comparative Analysis of Public Relations Web Performance of On-Premise and Cloud Computing Infrastructure with Round Robin Load Balancer Saleh, Robby Febrianur; Kusrini, Kusrini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2182

Abstract

Penelitian ini menganalisis perbandingan kinerja web humas untuk pelayanan publik antara infrastruktur on-premise dan cloud computing dengan load balancer round robin di RSUD Ratu Aji Putri Botung. Era digitalisasi mendorong rumah sakit mengoptimalkan sistem informasi termasuk web humas sebagai platform komunikasi dengan masyarakat. Metode penelitian menggunakan pendekatan eksperimental komparatif dengan pengujian beban menggunakan Apache JMeter pada tiga skenario: 50, 200, dan 2000 concurrent users. Parameter yang dianalisis meliputi response time, throughput, CPU utilization, memory usage, dan availability. Hasil penelitian menunjukkan cloud computing dengan load balancer round robin memberikan performa superior dengan response time excellent (215-293 ms) untuk semua skenario vs on-premise yang mengalami performance collapse hingga 111,969 ms pada 2000 users. CPU utilization cloud computing optimal (78-90%) dengan distribusi beban merata, sedangkan on-premise under-utilized (6-49%). Network traffic cloud computing consistent (354-356K bytes/sec) menunjukkan throughput predictable, sementara on-premise erratic (45-551K bytes/sec). Load balancer round robin terbukti highly effective dengan perfect success rate (100%) vs on-premise (99.3%). Cloud computing menunjukkan excellent scalability dan 497.8x lebih cepat pada extreme load. Penelitian merekomendasikan implementasi cloud computing untuk web humas rumah sakit guna meningkatkan kualitas pelayanan publik significantly
Optimasi Algoritma Convolutional Neural Network dengan Arsitektur Efficientnet-B0 dan Resnet-50 untuk Klasifikasi Jenis Sampah: Optimization of Convolutional Neural Network Algorithm with Efficientnet-B0 and Resnet-50 Architecture for Waste Type Classification Ardana, Wildan Muhammad; Kusrini, Kusrini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2030

Abstract

Penelitian ini mengembangkan sistem klasifikasi sampah otomatis menggunakan deep learning untuk membedakan sampah organik dan dapat didaur ulang. Penelitian dilakukan menggunakan dataset dari Kaggle yang terdiri dari 25.077 gambar sampah dengan dua kategori utama: organik (O) dan dapat didaur ulang (R). Metodologi penelitian berfokus pada perbandingan transfer learning menggunakan arsitektur EfficientNet-B0 dan ResNet-50. Teknik data augmentation modern (rotasi, zoom, flip) diterapkan untuk meningkatkan generalisasi model, dan Keras Tuner digunakan untuk optimasi hyperparameter secara sistematis. Hasil penelitian menunjukkan bahwa model EfficientNet-B0, setelah optimasi hyperparameter, mencapai performa terbaik dengan akurasi pengujian 97.25%. Arsitektur ini secara signifikan mengungguli ResNet-50 (akurasi 93.39%) dalam skenario perbandingan. Laporan klasifikasi detail untuk model terbaik menunjukkan kinerja yang sangat baik dan seimbang dalam mengklasifikasi sampah organik (presisi: 0.93, recall: 0.98) dan sampah dapat didaur ulang (presisi: 0.97, recall: 0.91). Waktu evaluasi yang cepat mengindikasikan potensi implementasi sistem secara real-time. Penelitian ini membuktikan efektivitas transfer learning dengan arsitektur modern yang dikombinasikan dengan optimasi hyperparameter untuk menciptakan solusi klasifikasi sampah otomatis yang sangat akurat dan efisien.
Optimasi Yolov11 Melalui Hyperparameter Tuning dan Data Augmentasi untuk Meningkatkan Akurasi Deteksi Kendaraan pada Kondisi Malam Hari: Yolov11 Optimization Through Hyperparameter Tuning and Data Augmentation to Improve Vehicle Detection Accuracy at Night Zulkarnain, Imam Alfath; Kusrini, Kusrini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2250

Abstract

Deteksi kendaraan pada malam hari menghadapi tantangan signifikan akibat pencahayaan rendah, silau lampu depan, dan kontras objek yang terbatas. Akurasi deteksi yang rendah pada malam hari menjadi penghambat utama dalam pengembangan sistem transportasi cerdas (ITS) dan sistem pengawasan lalu lintas yang andal secara 24/7. Penelitian ini bertujuan mengoptimalkan YOLOv11 untuk meningkatkan akurasi deteksi kendaraan dalam kondisi tersebut. Optimasi dilakukan melalui penyesuaian hiperparameter, termasuk pengaturan laju pembelajaran (0.001), momentum (0.937), dan weight decay (0.0005), serta penerapan teknik augmentasi data seperti penyesuaian saturasi dan kecerahan, translasi, skala, flipping horizontal, mosaic, dan mixup. Model diuji dalam dua skenario: (1) data malam hari dan (2) gabungan data siang dan malam. Hasil penelitian menunjukkan bahwa YOLOv11 yang telah dioptimalkan mencapai precision 0.97, recall 0.92, dan mAP@0.5 sebesar 0.97 pada skenario malam hari, melampaui kinerja YOLOv8 dan YOLOv11 baseline. Pada skenario gabungan, model tetap unggul dengan precision 0.95, recall 0.95, dan mAP@0.5 sebesar 0.98. Temuan ini membuktikan bahwa kombinasi penyesuaian hiperparameter dan augmentasi adaptif efektif meningkatkan kinerja deteksi kendaraan pada malam hari tanpa menurunkan akurasi pada kondisi siang. Pendekatan ini menjanjikan untuk diaplikasikan dalam sistem pemantauan lalu lintas berbasis visi komputer yang memerlukan konsistensi performa tinggi baik di siang maupun malam hari.
Meningkatkan Dataset CodeXGLUE dengan Representasi Abstract Syntax Tree (AST) Ter Seragam untuk Analisis Kode Lintas Bahasa Siswo Utomo, Mardi; Utami, Ema; Kusrini, Kusrini; Setyanto, Arief
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Dataset kode sumber populer seperti CodeXGLUE belum menyediakan representasi sintaksis yang diseragamkan untuk penelitian lintas bahasa pemrograman. Hal ini akan menyulitkan saat dilakukan penelitian yang berkaitan dengan analisis syntax-aware. Penelitian ini menyediakan representasi sintaksis yang diseragamkan untuk memperkaya dataset CodeXGLUE.  Kami menghadirkan dataset CodeXGLUE-AST (Abstract Syntax Tree) seragam untuk enam bahasa pemrograman: Go, Java, JavaScript, Python, Ruby, dan PHP. AST diekstraksi menggunakan Tree-sitter dan disimpan dalam format JSON terstruktur. Untuk menjaga konsistensi antar bahasa, kemudian dilakukan klasifikasi dan pemetaan tipe node guna menyatukan representasi struktur AST. Evaluasi dataset menggunakan analisis kelengkapan struktur AST, pengukuran akurasi rekonstruksi kode menggunakan skor BLEU, serta pengujian ekstraksi Data Flow Graph (DFG) untuk menjaga ketergantungan antar variabel. Selain itu juga dilakukan pengujian pada tugas peringkasan kode menggunakan model CodeT5 yang menunjukkan peningkatan nilai BLEU, METEOR, ROUGE dan ROUGE-L hampir disemua percobaan saat menggunakan AST yang diseragamkan. Dengan representasi AST yang telah diseragamkan, diharapkan pengembangan model ML multi bahasa yang lebih andal dan sadar sintaksis untuk tugas-tugas seperti klasifikasi kode, pembuatan ringkasan kode, dan rekonstruksi program akan menjadi lebih berkembang.   Abstract Popular source code datasets like CodeXGLUE have not yet provided a standardized syntactic representation for cross-programming language research. This data gap will complicate research related to syntax-aware analysis. This research provides a standardized syntactic representation to enrich the CodeXGLUE dataset. We present a uniform CodeXGLUE-AST (Abstract Syntax Tree) dataset for six programming languages: Go, Java, JavaScript, Python, Ruby, and PHP. The AST is extracted using Tree-sitter and stored in a structured JSON format. To maintain consistency across languages, classification and mapping of node types were then performed to unify the AST structure representation. The dataset evaluation used AST structure completeness analysis, code reconstruction accuracy measurement using BLEU scores, and Data Flow Graph (DFG) extraction testing to maintain variable dependencies. Additionally, testing was conducted on the code summarization task using the CodeT5 model, which showed an increase in BLEU, METEOR, ROUGE, and ROUGE-L scores in almost all experiments when using the standardized AST. With the standardized AST representation, it is hoped that the development of more reliable and syntax-aware multilingual ML models for tasks such as code classification, code summarization, and program reconstruction will become more advanced.
The effectiveness of using RFID and IoT in digital transformation processes in garment companies using the UTAUT model2 Sentoso, Thedjo; Kusrini, Kusrini; Hanafi, Hanafi
Gema Wiralodra Vol. 14 No. 2 (2023): gema wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v14i2.511

Abstract

This study aims to analyze the effectiveness of using RFID and IoT in the digital transformation process in a garment company using the UTAUT2 model. This research is necessary because it can influence the intentions and behavior of its users to increase production effectiveness. A quantitative approach uses the survey method used in this study to achieve the research objectives. The number of respondents in this study was 193 employees who worked in the preparation area. The data collected from the questionnaire results were analyzed using inferential statistics. The study results show that employee acceptance of using RFID and IoT in the digital transformation process gets a positive response. Each variable average value used is in the value range 3.79 – 4.44 (scale 1 to 5). In addition, it was found that Performance Expectancy, Effort Expectation, and Price Value positively influenced Behavioral Intention. In contrast, Habit and Behavioral Intention positively influenced Use Behavioral. As for the Social Influence and Hedonic Motivation variables on Behavioral Intentions and the Facilitating Conditions variable on Usage Behavior, no positive effect was found.
Klasterisasi Pasien Rawat Jalan di Puskesmas dengan Menggunakan Metode Algoritma Clustering K-Means Azkar, Azkar; Kusrini, Kusrini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1832

Abstract

The Aikmel Utara Public Health Center in Lombok Timur Regency began operating in mid-2019, but has never utilized patient data to obtain information as a basis for decision-making efforts to improve quality of health services and consequently enhance patient satisfaction. The target respondents patient satisfaction survey at the Public Health Center are visitors, the majority of whom are outpatient visitors. The purpose of this research is to group outpatient patients based on variables such as gender, age, participation status in the BPJS health insurance program and patient address, as well as diagnosis of the patient's disease using the k-means clustering algorithm method with the assistance of the RapidMiner application. Patient data totaling 1889 were grouped into 2 clusters, 3 clusters, 4 clusters, and 5 clusters, and evaluated using the Davies-Bouldin Index (DBI). The research results show that the number of clusters formed is 2, with cluster 1 consisting of 1570 data and cluster 2 consisting of 319 data. Cluster 1 is dominated by female patients (1074 or 68.4%), BPJS participants (819 or 52.2%), with the most common age group being adults (883 or 56.2%), and most of them are from Toya village (488 or 31.1%), with the most common diagnosis being acute respiratory infections (J06) (223 or 14.2%). Meanwhile, cluster 2 is dominated by female patients (205 or 64.3%), BPJS participants (202 or 63.3%), with the most common age group being adults (191 or 59.9%), and most of them are from Toya village (108 or 33.9%), with the most common diagnosis being pregnancy examinations (Z34) (29 or 9.1%). From these cluster results, it is obtained that the majority of outpatient visitors at the Aikmel Utara Public Health Center are from Toya village and are dominated by the adult age group and BPJS health insurance participants, with the most common disease being acute respiratory infections. It is hoped that this information can assist the Public Health Center in making decisions or policies related to health programs in its working area
Distributed Denial Of Service (DDOS) Attack Detection On Zigbee Protocol Using Naive Bayes Algoritm Masud, Ibnu; Kusrini, Kusrini; Prasetio, Agung Budi
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.085 KB) | DOI: 10.29099/ijair.v5i2.214

Abstract

Distributed Denial of Service or better known as DDoS is an attempted attack from several computer systems that target a server so that the amount of traffic becomes too high so that the server cannot handle the request. DDoS is usually done by using several computer systems that are used as sources of attacks. So they attack one server through several computers so that the amount of traffic can also be higher. A DDoS attack is like a traffic jam that prevents a driver from reaching their desired destination on time. According to data, 33% of businesses in the world have fallen victim to DDoS attacks. DDoS is hard to trace. Some types of DDoS attacks can be very powerful and even reach speeds of 1.35 Tbps. Additionally, DDoS attacks can cause losses of $ 40,000 per hour if they occur. ZigBee is a standard from IEEE 802.15.4 for data communication on personal consumer devices as well as for business scale. ZigBee is designed with low power consumption and works for low level personal networks. ZigBee devices are commonly used to control another device or as a wireless sensor. ZigBee has a feature which is able to manage its own network, or manage data exchange on the network [1]. Another advantage of ZigBee is that it requires low power, so it can be used as a wireless control device which only needs to be installed once, because only one battery can make ZigBee last up to a year. In addition, ZigBee also has a "mesh" network topology so that it can form a wider network and more reliable data. In the previous research of Muhammad Aziz, Rusydi Umar, Faizin Ridho (2019) based on the results of the analysis carried out that the attack information that has been detected by the IDS based on signatures needs to be reviewed for accuracy using classification with statistical calculations. Based on the analysis and testing carried out with the artificial neural network method, it was found that the accuracy was 95.2381%. The neural network method can be applied in the field of network forensics in determining accurate results and helping to strengthen evidence at trial. The Naïve Bayes model performed relatively poor overall and produced the lowest accuracy score of this study (45%) when trained with the CICDDoS2019 dataset [47]. For the same model, precision was 66% and recall was 54%, meaning that almost half the time, the model misses to identify threats. 
Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ikhwanudin, Aolia Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni