Garuda - Garba Rujukan Digital

Sistem Monitoring Dan Evaluasi Order Pada PT. Telkom Akses Devisi Provisioning BGES Magelang Santoso, Kartika Imam; Satriya, Ilham Jati; Sundari, Cisilia
Jurnal Sistem Informasi (JASISFO) Vol. 3 No. 2 (2022): September 2022
Publisher : Politeknik Negeri Sriwijaya

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With the number of requests for new pairs with an average number of 90 new pairs orders per month, there is a need for continuous evaluation of employee performance and performance related to Time To Delivery orders to ensure the achievement of the goals that have been set. Managers and Leaders have been supervising the performance of Time to Delivery orders and employee performance by asking the DSS (Decision Support System) section which handles BGES order performance data. Then the DSS (Decision Support System) performs a data search to present it in the form of a report to the manager and his staff. This makes Managers and Leaders unable to see employee performance and monitor the handling of new network installations directly. The purpose of this research is to design and build a web-based order monitoring information system at PT. Telkom Access Magelang on the BGES Provisioning division. The system development method used is the Waterfall model. The modeling in the system design used is DFD and ERD modeling while making the website using the PHP programming language, Laravel Framework, and MySQL as database manager. The result of this research is a Monitoring and Evaluation Information System that can provide information about the daily order performance to Managers and Leaders at PT Telkom Access the BGES Provisioning Division.

SISTEM INFORMASI AJUAN KREANOVA DI BAPPEDA DAN LITBANGDA KABUPATEN MAGELANG Santoso, Kartika Imam; Yusuf , Hikmaturridho; Wahyudiono, Sugeng
Jurnal Sistem Informasi (JASISFO) Vol. 4 No. 2 (2023): September 2023
Publisher : Politeknik Negeri Sriwijaya

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Penelitian ini bertujuan agar dapat merancang dan membangun Sistem Informasi Ajuan Kreanova Berbasis Web Di Bappeda Dan Litbangda Kabupaten Magelang. Â Pemrograman yang digunakan adalah bahasa pemrograman PHP dan DBMS nya adalah MySQL. Sistem informasi ini sebagai media informasi dan pelayanan pengajuan inovasi (Kreanova) sehingga memudahkan proses pengajuan dan mengelola data Kreanova. Metode pengembangan sistem yang digunakan adalah metode pengembangan sistem Waterfall. Perancangan yang digunakan pada penelitian ini adalah pemodelan DFD (Data Flow Diagram). Hasil penelitian ini adalah berupa Sistem Informasi Ajuan Kreanova Berbasis Web di Bappeda Dan Litbangda Kabupaten Magelang untuk memudahkan proses pengajuan dan mengelola data data Kreanova.

Sistem Penjualan Pakaian Online "tukuCALAMBY" Anjar Septinegara; Neda Cisya Tama, Freshma; Rodliyati Karima, Isyatin; Imam Santoso, Kartika; Malita Puspita Arum, Dhika
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.26

The development of information and communication technology has changed the way consumers shop, especially in the fashion and clothing industry. This study aims to develop an online clothing ordering system called “tukuCALAMBY.” This system is useful for improving the efficiency of the sales process and providing convenience for customers when shopping. The system is designed using a web-based approach with two main actors, namely the admin and the customer. The system development method employs the Software Development Life Cycle (SDLC) approach using the Waterfall model by Sommerville. The design utilizes system modeling with the Unified Modeling Language (UML). The development results demonstrate that the “tukuCALAMBY” system successfully integrates features for managing product data, ordering, payment, and reporting into a single user-friendly platform. This system provides an effective solution to expand market reach and improve operational efficiency for online clothing stores. User Acceptance Testing (UAT) involving 20 users yielded a testing result of 92%.

TRANSFORMASI DIGITAL UMKM PERCETAKAN: OPTIMALISASI PLATFORM ECOMMERCE TERINTEGRASI PADA ESPRINT.STORE Nabil, Muhammad Nabil Musyarof; Musyarof, Muhammad Nabil; Kisnandhya Putra, Afif; Naufal Islam, Nibroos; Dwi Astuti, Rizky; Imam Santoso, Kartika; Triyono, Andri
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.27

Digital transformation has become a strategic necessity for Micro, Small, and Medium Enterprises (MSMEs), particularly in the printing sector which demands speed, flexibility, and personalized services. This study aims to examine the effectiveness of the esprint.store platform as a web-based eCommerce solution integrated with WhatsApp API and a dynamic pricing system. A mixed-method approach was employed, combining Google Analytics data, a System Usability Scale (SUS) questionnaire from 120 respondents, and system architecture observation. The results indicate a 35% increase in sales conversion and a reduction in customer response time from 24 hours to 15 minutes. These findings suggest that digitalization through a simple yet functional system can enhance service efficiency and customer satisfaction within the MSME.

AI-BAHSI: Metode Hibrid Artificial Intelligence-Behavioral Analysis dan Hybrid Security Intelligence untuk Deteksi dan Mitigasi Ancaman Real-time pada Wireless Access Point Emmanuel, Rheimanda Devin Emmanuel; Emmanuel, Rheimanda Devin; Anggraini, Ani; Condro Wibowo, Agus; Imam Santoso, Kartika; Supriyadi, Eko
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.30

Wireless access point (AP) security faces significant challenges with the emergence of sophisticated attacks such as SSID Confusion (CVE-2023-52424), KRACK attacks, and advanced persistent threats. This research develops a hybrid AI-BAHSI (Artificial Intelligence-Behavioral Analysis and Hybrid Security Intelligence) method that integrates deep learning, ensemble machine learning, and federated learning for real-time threat detection and mitigation on wireless access points. The proposed method combines Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for pattern recognition, Random Forest-Support Vector Machine ensemble for threat classification, and federated learning for privacy-preserving security intelligence. Evaluation was conducted on a synthetic dataset that includes 15,000 normal traffic samples and 8,500 attack samples of various types. The results show that AI-BAHSI achieves a detection accuracy of 98.7%, a precision of 97.3%, a recall of 98.1%, and an F1-score of 97.7% with a false positive rate of only 1.2%. This method successfully detected zero-day attacks with a 94.6% confidence level and was able to automatically mitigate them in an average of 0.8 seconds. The main contribution of this research is the development of an adaptive security framework that can learn from new attack patterns in real time while preserving privacy through a federated learning architecture.

SMART-GUARD: Self-adaptive Multi-Agent Reinforcement learning Threat Guard dengan Game Theory dan Consensus Mechanisms untuk Enhanced Wireless Access Point Security Aprilianto, Dwi Kurniawan; Yusuf Mufarihin, Ahmad; Najhan Atifa, Akhie; Supriyadi, Eko; Imam Santoso, Kartika
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.32

Kompleksitas serangan cyber terhadap wireless access point semakin meningkat dengan munculnya adversarial AI dan coordinated attack scenarios. Penelitian ini mengembangkan framework SMART-GUARD (Self-adaptive Multi-Agent Reinforcement learning Threat Guard) yang mengintegrasikan multi-agent reinforcement learning (MARL), game theory, dan consensus mechanisms untuk membangun sistem pertahanan adaptif dan kolaboratif. Framework yang diusulkan menggabungkan Deep Q-Networks (DQN) dengan hierarchical multi-agent architecture, Stackelberg game untuk strategic defense planning, Self-Organizing Maps (SOM) untuk threat clustering, dan Byzantine-fault tolerant consensus untuk koordinasi terdistribusi. Evaluasi dilakukan pada testbed yang mensimulasikan 20 access points dengan 500 client devices dan 15 jenis serangan berbeda. Hasil eksperimen menunjukkan SMART-GUARD mencapai defense success rate 97.4%, mean response time 1.2 detik, dan resource utilization efficiency 89.3%. Framework ini mampu beradaptasi dengan 12 jenis zero-day attacks dengan confidence level 92.8% dan menunjukkan scalability yang superior hingga 1000+ access points. Kontribusi utama penelitian ini adalah pengembangan self-adaptive defense ecosystem yang dapat melakukan strategic decision making secara autonomous melalui game-theoretic analysis dan koordinasi multi-agent yang fault-tolerant.

Expert System For Corn Plant Disease Diagnosis Using Hybrid Fuzzy Tsukamoto And Naive Bayes Method Kartika Imam Santoso; Eko Supriyadi; Andri Triyono; Dhika Malita Puspita
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p141-155

Corn is a strategic food commodity in Indonesia, with production of 22.44 million tons in 2023. However, disease attacks can cause productivity declines of up to 30-80%, mainly from downy mildew, leaf rust, and leaf spot. The limited number of pathology experts in the field leads to delayed diagnosis, resulting in significant economic losses for farmers. This research aims to develop an expert system for diagnosing corn plant diseases using a hybrid Fuzzy Tsukamoto and Naive Bayes method to enhance diagnosis accuracy, taking into account uncertainty in symptom severity levels. The system was developed using Durkin's Expert System Development Life Cycle (ESDLC), which consists of six phases. A knowledge base was built from SINTA and Scopus-indexed literature, identifying five diseases and 17 symptoms. The fuzzy Tsukamoto method was employed for the fuzzification of symptom severity, utilizing three membership functions (intensity, coverage, and severity), after which Naive Bayes calculated the posterior probability. The hybrid score was calculated with 40% Fuzzy and 60% Bayes weights. The system was successfully developed with an interactive web interface. Accuracy testing using 30 validation cases yielded an accuracy of 86.67%, with 85% sensitivity and 88% specificity. Expert testing by three plant pathology experts gave excellent ratings (average 4.6/5.0) for diagnosis accuracy, knowledge base completeness, and usability aspects. The hybrid Fuzzy Tsukamoto and Naive Bayes method is effective for diagnosing corn plant diseases, achieving 86.67% accuracy, which is 6.67% higher than the Certainty Factor method and 11.67% higher than the single Naive Bayes method. This system can help farmers perform early diagnosis and reduce dependence on experts.

Klasifikasi Diabetes Melitus Menggunakan Metode Decision Tree Dan C4.5 Di Kabupaten Grobogan Saniya, Zahra Fatma; Supriyadi, Eko; Santoso, Kartika Imam
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.422

Penelitian ini muncul karena tingginya kasus diabetes yang jadi masalah kesehatan penting di masyarakat. Deteksi awal dengan metode klasifikasi penting agar penanganan medis bisa lebih cepat dan tepat. Penelitian ini bertujuan untuk tahu seberapa akurat algoritma Decision Tree dan C4.5 dalam mengklasifikasi diabetes, cari faktor utama yang pengaruhi klasifikasi, serta analisis potensi bias dalam data.Data riset didapat dari posyandu dan kuesioner masyarakat di Kecamatan Penawangan, Grobogan. Proses klasifikasi dilakukan pakai aplikasi RapidMiner dengan tahapan persiapan data, buat model, hingga evaluasi pakai *confusion matrix*.Hasil riset menunjukkan algoritma Decision Tree C4.5 hasilkan akurasi 96,49%. Faktor terkuat yang pengaruhi status diabetes yaitu umur, tinggi badan, dan riwayat keluarga. Hasil riset ini tunjukkan algoritma C4.5 bisa dipakai jadi metode yang baik untuk bantu klasifikasi diabetes. Dengan begitu, riset ini harapannya bisa sumbangsih dalam pengembangan sistem deteksi awal dan upaya pencegahan serta edukasi masyarakat tentang bahaya diabetes.

Klasifikasi Diabetes Melitus Menggunakan Metode Decision Tree Dan C4.5 Di Kabupaten Grobogan Saniya, Zahra Fatma; Supriyadi, Eko; Santoso, Kartika Imam
Go Infotech: Jurnal Ilmiah STMIK AUB Vol 31, No 2 (2025): December
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer AUB - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36309/goi.v31i2.422

Penelitian ini muncul karena tingginya kasus diabetes yang jadi masalah kesehatan penting di masyarakat. Deteksi awal dengan metode klasifikasi penting agar penanganan medis bisa lebih cepat dan tepat. Penelitian ini bertujuan untuk tahu seberapa akurat algoritma Decision Tree dan C4.5 dalam mengklasifikasi diabetes, cari faktor utama yang pengaruhi klasifikasi, serta analisis potensi bias dalam data.Data riset didapat dari posyandu dan kuesioner masyarakat di Kecamatan Penawangan, Grobogan. Proses klasifikasi dilakukan pakai aplikasi RapidMiner dengan tahapan persiapan data, buat model, hingga evaluasi pakai *confusion matrix*.Hasil riset menunjukkan algoritma Decision Tree C4.5 hasilkan akurasi 96,49%. Faktor terkuat yang pengaruhi status diabetes yaitu umur, tinggi badan, dan riwayat keluarga. Hasil riset ini tunjukkan algoritma C4.5 bisa dipakai jadi metode yang baik untuk bantu klasifikasi diabetes. Dengan begitu, riset ini harapannya bisa sumbangsih dalam pengembangan sistem deteksi awal dan upaya pencegahan serta edukasi masyarakat tentang bahaya diabetes.

Artificial Intelligence-Based Automatic Text Detection System Using Multi-Layer Pattern Recognition Kartika Imam Santoso; Santoso, Kartika; Edi Widodo; Theresia Widji Astuti
Jurnal Transformatika Vol. 23 No. 2 (2026): January 2026
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i2.13256

The rapid advancement of generative AI models such as ChatGPT, Claude, and Gemini raises serious concerns about the authenticity of academic and professional documents. This study develops a detection system that uses a combination of linguistic, structural, and statistical pattern analysis to identify AI-generated text and classify the responsible AI model. The system analyzes more than 12 different parameters from uploaded documents (PDF, DOCX, TXT formats). The detection engine operates through seven analytical layers: signature detection, linguistic analysis, word pattern analysis, structural analysis, feature pattern analysis, vocabulary and grammar assessment, and AI fingerprinting. The scoring mechanism provides a general AI probability score (0-100%) and individual probability scores for 10 different AI models. In testing with 100 documents, the system achieved 76.8% accuracy in identifying AI-generated text and 87.3% accuracy in classifying the source AI model. Sentence entropy analysis, paragraph uniformity assessment, and distinctive linguistic markers proved most effective. This study demonstrates that multi-layer pattern recognition is a viable approach for detecting and classifying AI-generated text, with implications for academic integrity, content verification, and digital forensics.

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