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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Pengabdian kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi IPSSJ Jurnal Ilmu Komputer
Arya Adhyaksa Waskita
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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MELINDUNGI KEBERLANJUTAN IOT EDUKASI KEAMANAN BAGI SISWA SMAN 1 JASINGA Pebrianto, Juri; Arya Adhyaksa Waskita
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2024): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

This PKM highlights the issue of Internet of Things (IoT) security in everyday life, especially among the students of IT Club SMAN 1 Jasinga. Involving a series of workshops, practical training, discussion sessions, and the creation of educational materials, the programme aimed to improve IoT security understanding and skills. The results include increased student knowledge, practical skills in securing devices and networks, and a heightened awareness of the importance of IoT security. The educational materials produced can be accessed by students and the community, while the positive contributions of the programme are expected to be published in a service journal. In addition, this PKM includes service to partners in the field of productive economy, with a focus on production evaluation, modern agriculture training, measurement and monitoring of production results, as well as identification of management problems and management training. The rationale of this PKM is to provide an in-depth understanding of IoT security, improve students' readiness to face the complexity of the digital world, and create change agents who share knowledge in the community. The PKM objectives involved understanding, practical skills, awareness, and creation of educational materials. The results showed students' enthusiasm, especially regarding the discussion of "hackers," while the conclusion confirmed that the activities were carried out according to the target and plan. Thus, this PKM is expected to provide concrete and sustainable benefits for the IT Club students of SMAN 1 Jasinga as well as contribute to a better understanding of IoT security in the community.
PELATIHAN MEMBUAT KARYA TULIS ILMIAH DENGAN MEMANFAATKAN ARTIFISIAL INTELIGENCE (AI) BAGI PELAJAR SMK BINUSA KOTA TANGERANG SELATAN Tukiyat; Sajarwo Anggai; Arya Adhyaksa Waskita; Rafi Mahmud Zain; Muhammad Rafif Misbahuddin
Abdi Jurnal Publikasi Vol. 3 No. 2 (2024): November
Publisher : Abdi Jurnal Publikasi

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Abstract

Scientific writing is an essential skill that students, particularly those in Vocational High Schools (SMK), need to master. However, many students face challenges in understanding research methodology and technical skills, often resulting in scientific works that do not meet academic standards. This study aims to enhance students' scientific writing skills through training based on Artificial Intelligence (AI) technology. This Community Service Program (PKM) was conducted at SMK Binusa, South Tangerang City, involving 22 participants from the Computer Network Engineering department. The implementation method consisted of theory sessions, practical sessions, and workshop discussions. Evaluation was carried out using pre-tests and post-tests to measure participants' understanding improvements. The training results revealed that most participants showed enhanced scientific writing abilities, with 43% rated as excellent and 31% as good. Additionally, the training materials and instructor quality were positively evaluated, with 57% of participants rating the materials as excellent and 60% rating the instructors as excellent. This training provided significant benefits, particularly in fostering innovation and participants' digital literacy skills. Nevertheless, several areas for improvement were identified, such as delivering more interactive materials and diversifying training methods. This program recommends integrating interactive technology and continuous training to support the development of scientific writing skills for vocational students, thereby enhancing their competitiveness in the future.
Analisis Performa Sistem Deteksi Intrusi pada Jaringan IoT Menggunakan Ensemble Learning dan Decision Tree Kamaludin Jumaidi; Arya Adhyaksa Waskita; Ahmad Musyafa
Integrative Perspectives of Social and Science Journal Vol. 2 No. 2 April (2025): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

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Serangan siber, terutama Distributed Denial of Service (DDoS), mengancam keamanan jaringan Internet of Things (IoT). Penelitian ini menganalisis performa sistem deteksi intrusi menggunakan ensemble learning (LightGBM, XGBoost, CatBoost) Decision Tree dan Random Forest pada dataset CIC-BCCC-NRC TabularIoTAttack-2024, yang mencakup serangan seperti DDoS TCP Flood, MITM ARP Spoofing, dan Reconnaissance Scan. Proses melibatkan pra-pemrosesan dengan SMOTE untuk menyeimbangkan kelas dan seleksi fitur menggunakan SHAP. Tantangan utama adalah deteksi kelas Recon Port Scan dan Recon OS Scan akibat tumpang tindih fitur. Hasil menunjukkan Ensemble mencapai akurasi 99,21%, F1-score 99,21%, dan log loss 0,0672, diikuti LightGBM sebagai model tunggal dengan akurasi 97,58% dan log loss 0,0795. Decision Tree memiliki akurasi (86,48%) terendah dibanding model lain akibat overfitting. LightGBM lebih efisien (inference time 292,43 detik) dibandingkan Ensemble (891,04 detik), menjadikannya solusi optimal untuk deteksi serangan IoT. 
PELATIHAN DESAIN GRAFIS APLIKASI CANVA UNTUK MENINGKATKAN KREATIVITAS DAN LITERASI DIGITAL BAGI SISWA-SISWI SMK ISLAM PERMATASARI 2 RUMPIN BOGOR Tukiyat, Tukiyat; Sajarwo Anggai; Arya Adhyaksa Waskita; Rafi Mahmud Zain
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 4: September 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i4.8486

Abstract

Pelatihan desain grafis dengan Canva di SMK Islam Permatasari 2 Rumpin Bogor bertujuan meningkatkan kreativitas dan literasi digital siswa. Program ini mengatasi kendala dalam memahami fitur Canva, mengembangkan ide desain, serta keterbatasan akses perangkat dan internet. Metode pelatihan meliputi pengenalan dasar Canva, demonstrasi praktis, dan kolaborasi.Evaluasi menunjukkan 28,57% peserta berasal dari jurusan multimedia dan 71,43% dari teknik komputer dan jaringan, dengan 57,14% laki-laki dan 42,86% perempuan. Sebanyak 80,95% peserta menilai kegiatan sangat baik, dan 84,29% puas dengan penyampaian materi. Kreativitas peserta meningkat dengan 52,38% menilai peningkatan sangat baik, dan literasi digital mencapai 82,54%. Meski demikian, 9,52% merasa pelatihan belum sepenuhnya menumbuhkan daya inovatif. Secara keseluruhan, pelatihan ini berhasil meningkatkan keterampilan desain grafis siswa dengan 81,9% dalam pengembangan kreativitas dan 84,76% dalam penilaian materi. Hasil pelatihan ini memberikan kontribusi positif dalam meningkatkan keterampilan desain grafis dan literasi digital, meskipun masih ada ruang untuk perbaikan dalam interaksi dan bimbingan. Saran untuk program mendatang mencakup evaluasi berkelanjutan, integrasi Canva ke dalam kurikulum, serta peningkatan aksesibilitas teknologi di sekolah. Pelatihan ini tidak hanya meningkatkan kompetensi siswa secara individu, tetapi juga berkontribusi pada peningkatan kualitas sumber daya manusia di era digital.
Analisis Sentimen Pembangunan IKN (Ibu Kota Nusantara) Pada Twitter Menggunakan Metode K- Nearest Neighbor, Naive Bayes Dan Support Vector Machines Prasmono, Yossy Veiebrian Fitri; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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This research investigates the Nusantara Capital City (IKN) relocation, which has generated diverse opinions, including concerns over the chosen location and the swift ratification of related laws. Recently, the Indonesian government has called on the public to support IKN's development. To assess public sentiment regarding this relocation, sentiment analysis was performed on a dataset of tweets. After data cleaning, 502 tweets were analyzed, yielding 337 positive and 163 negative comments. The analysis utilized Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (K-NN) algorithms, incorporating feature selection through Particle Swarm Optimization (PSO). This study compares the performance of Naive Bayes, SVM, and K-NN without feature selection against those methods with feature selection, specifically analyzing their Area Under Curve (AUC) values to identify the most effective algorithm. The results indicate that the PSO-based SVM algorithm achieved the highest performance, with an accuracy of 97.63% and an AUC of 0.997. This research successfully identifies an optimal algorithm for classifying positive and negative comments regarding the relocation of the Nusantara Capital City, contributing valuable insights to public sentiment analysis in this context.
Analisis Kinerja Sistem Deteksi Intrusi Jaringan Internet Of Things Berbasis Metode Ensemble Eko Kristianto; Arya Adhyaksa Waskita; Thoyyibah Tanjung
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Abstract

Network intrusion has rapidly evolved, posing significant risks to IT infrastructure. To address this, ensemble learning, known for its robust classification capabilities, is applied to IoT network traffic using the public RT_IOT2022 dataset. Models such as CatBoost, Extreme Gradient Boost (XGBoost), and LightGBM were developed and evaluated. The dataset was normalized using the Normalizer and MinMaxScaler functions from the scikit-learn framework. Model training was conducted with an 80:20 fixed data split for training and testing, along with 5-fold cross-validation. Testing revealed that XGBoost with MinMaxScaler and the 80:20 split achieved the highest accuracy of 99.89%. However, accuracy decreased to 94.04% when using 5-fold cross-validation. Nevertheless, XGBoost with MinMaxScaler consistently demonstrated the fastest computation time across all schemes. For instance, it required only 15 seconds for the fixed split scheme compared to 59 seconds for 5-fold cross-validation. These findings highlight the efficiency and accuracy of XGBoost when combined with MinMaxScaler under specific validation schemes.
Analis Perancangan Dan Penerapan Keamanan Jaringan Menggunakan Metode Intrusion Detection System (IDS), Intrusion Prevention System (IPS) Dan Demilitarized Zone (DMZ) Pada PT. Maha Digital Indonesia (Mahapay) Trijanitra, Evan; Arya Adhyaksa Waskita; Taswanda Taryo
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Abstract

Network security systems, in recent years have become the main focus in the world of securing other important data, this is due to the high number of suspicious threats (Suspicious Threats) and attacks from the Internet. Network security involves efforts to protect data and computer systems from detrimental threats, such as cyberattacks, malware, and data theft. The existence of increasingly complex and evolving threats has increased awareness of the need for strong network security. PT. Maha Digital Indonesia (Mahapay) is a company operating in the field of EDC Field Service where it is very important that client data is kept confidential. This requires good network security to maintain the confidentiality of the data. So the aim of this research is to implement network security using the Intrusion Detection System (IDS), Intrusion Prevention System (IPS) and Demilitarized Zone (DMZ) methods as network security at PT . Maha Digital Indonesia (Mahapay). The results of this research are the formation of connections between networks in the topology along with the successful functioning of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) detecting and preventing suspicious activities carried out by attackers and the operation of rules for the DMZ area. Success in the application is tested again by carrying out several attack methods that will be analyzed such as Syn Flood Attack, Ping Of Death and Port Scanning which will be handled by the configuration that has been applied to the network and server.
Optimizing Learning Rate, Epoch, and Batch Size in Deep Learning Models for Skin Disease Classification Rahman, Taufiqur; Anggai, Sajarwo; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

This study explores the best combination of learning rate, number of epochs, and batch size for training deep learning models to classify skin diseases. The experiments involved analyzing how loss changes with learning rates on a logarithmic scale. The findings reveal that a learning rate of approximately 10-2 is most effective, with 5×10−3 offering additional stability during training. Various combinations of epochs and batch sizes were tested, ranging from 20 to 100 epochs and batch sizes between 32 and 128. The results show that using a batch size of 32 yielded the best outcomes, achieving a validation accuracy of 97.35% and the lowest validation loss of 0.1074. While a batch size of 128 was more efficient in terms of time, it resulted in slightly lower accuracy. The model performed optimally with 25 epochs and a batch size of 32, avoiding any signs of overfitting. Data preparation also played a crucial role, involving steps like image resizing, pixel normalization, and data augmentation to align with the requirements of models such as VGG-19, Inception-V4, and ResNet-152. Visualizing the dataset distribution ensured data quality and class balance, allowing the model to better recognize patterns. This study offers practical insights for effectively and efficiently training deep learning models, particularly for tasks related to skin disease classification.
Analisis Sentimen Terhadap Istana Garuda Di Ibukota Nusantara (IKN) Menggunakan Algoritma Random Forest Dan Support Vektor Machine Jihansyah, Muhamad; Agung Budi Susanto; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

ABSTRACT The relocation of Indonesia's capital city (IKN) to East Kalimantan is a national strategic project that has sparked diverse public opinions, particularly regarding the construction of Garuda Palace. This study aims to analyze public sentiment toward the Garuda Palace project using Random Forest and Support Vector Machine (SVM) algorithms and to compare their performance based on accuracy, precision, recall, and F1-score. This research offers three key novelties. First, it focuses on public opinion regarding the Garuda Palace project at IKN, which is underexplored in both local and international literature. Second, the use of Inset and Senti labeling techniques introduces a novel approach to sentiment categorization. Third, the comprehensive evaluation of Random Forest and SVM performance provides new insights into their effectiveness in large-scale infrastructure sentiment analysis in Indonesia. The methodology consists of five stages: (1) Data collection through web scraping from Twitter (July-August 2024) using keywords related to "Garuda Palace" and "IKN"; (2) Data preprocessing, including tokenization, stopword removal, stemming, and TF-IDF transformation; (3) Data labeling using Inset and Senti approaches; (4) Model training with Random Forest and SVM algorithms; (5) Model evaluation using confusion matrices and performance metrics such as accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved 77% (Inset) and 89% (Senti) accuracy, excelling in detecting negative sentiment with an F1-score of 0.93 on the Senti dataset. SVM achieved 89% (Inset) and 91% (Senti) accuracy, performing better in detecting positive sentiment with a precision of 0.96 on the Senti dataset. This study provides valuable insights into public perceptions of national infrastructure projects, supports data-driven decision-making, and serves as a reference for future sentiment analysis systems
Analisis Sentimen Pengguna X Terhadap Pemilihan Gubenur Dki Jakarta Tahun 2024 Dengan Algoritma Naïve Bayes, K-Nearest Neighbor Dan Decision Tree Syarif Hidayatullah; Arya Adhyaksa Waskita; Achmad Hindasyah
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 1 (2026): IKRAITH-INFORMATIKA Vol 10 No 1 Maret 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i1.6283

Abstract

The 2024 DKI Jakarta Gubernatorial Election is one of the main political agendas that attracts public attention in Indonesia. In this context, sentiment analysis of the Gubernatorial and Vice Gubernatorial candidates is important to understand public opinion. With the advancement of technology, especially the internet and social media platforms like X (formerly Twitter), the public can freely express their opinions regarding the candidates. This study aims to analyze public sentiment towards the three pairs of Gubernatorial and Vice Gubernatorial candidates of DKI Jakarta through tweets collected using the crawling method, resulting in 6,120 rows of data, with each candidate pair obtaining 2,040 rows of data. The accuracy of sentiment analysis was calculated using Naïve Bayes, K-Nearest Neighbor, and Decision Tree algorithms to compare the accuracy of these three algorithms with an 80:20 testing-training data split and feature extraction using TF-IDF and Transformer, implemented using the RapidMiner software. Based on the analysis results, Naïve Bayes with TF-IDF representation showed the highest accuracy for Paslon 1 at 86.76%, followed by Paslon 2 at 76.96%, and Paslon 3 at 72.79%. Meanwhile, K-NN with TF-IDF achieved the best results for Paslon 1 (67.40%) and Paslon 2 (71.32%), while Decision Tree achieved the highest accuracy for Paslon 1 at 72.55%. For the Transformer representation, the overall accuracy was lower compared to TF-IDF, with Paslon 1 achieving 56.86%, Paslon 2 at 53.43%, and Paslon 3 at 51.23%. These results indicate that TF-IDF is more effective for sentiment analysis of tweets related to the Gubernatorial candidates, with Naïve Bayes being the most accurate algorithm.
Co-Authors Achmad Hindasyah AGUNG BUDI SUSANTO Ahmad Musyafa Anggai, Sajarwo Eko Kristianto Jihansyah, Muhamad Kamaludin Jumaidi Muhammad Rafif Misbahuddin Pebrianto, Juri Prasmono, Yossy Veiebrian Fitri Rafi Mahmud Zain Syarif Hidayatullah Taswanda Taryo Taufiqur Rahman, Taufiqur Thoyyibah Tanjung Trijanitra, Evan Tukiyat Tukiyat Tukiyat, Tukiyat