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All Journal Jurnal Informatika dan Teknik Elektro Terapan Psychology, Evaluation, and Technology in Educational Research Progresif: Jurnal Ilmiah Komputer Jurnal Sistem Komputer & Kecerdasan Buatan Urecol Journal. Part E: Engineering JOINCS (Journal of Informatics, Network, and Computer Science) Journal of Innovative and Creativity Journal of Computer Science Advancements Jurnal Abdimas Indonesia Jurnal Bisnis Digital dan Sistem Informasi Jurnal Ilmu Komputer dan Matematika
Achmad Ridwan
UNiversitas Muhammadiyah Kudus, Indonesia
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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DETEKSI DINI ASD(AUTISM SPECTRUM DISORDER) MENGGUNAKAN MACHINE LEARNING Taftazani Ghazi Pratama; Achmad Ridwan; Agung Prihandono
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 2 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i2.1998

Abstract

Deteksi dini ASD pada seorang balita sangat membantu orang tua untuk mengetahui kembang tumbuh anak. Pada penelitian ini bertujuan untuk  deteksi dini ASD  menggunakan Naive Bayes dan KNN yang diterapkan pada dataset Autism screening data for toddlers.  Penelitian ini dilakukan melalui tahapan pra pengolahan, pembagian data training 80% dan testing 20%, pembuatan model, dan evaluasi dari model yang dibuat.  Hasil evaluasi model yang dibuat  menunjukkan bahwa KNN memperoleh nilai akurasi, sensitivitas, dan spesifisitas lebih tinggi daripada Naive Bayes. KNN memperoleh nilai akurasi sebesar  95,73%, sensitivitas sebesar 93,84%, dan  spesifisitas100%. Hal ini mengindikasikan bahwa KNN dapat membantu dalam deteksi dini pada seorang balita dengan kinerja yang baik. 
Bagaimanakah Pengaruh Data Analytics Terhadap Pengembangan Pemasaran di Era Digital Achmad Ridwan; Edwin Sugesti Nasution; Loso Judijanto; Eka Adnan Agung; Nadia Dwi Irmadiani
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.3732

Abstract

Era digital telah membawa perubahan mendasar dalam strategi pemasaran, di mana data analytics menjadi fondasi utama untuk mendukung pengambilan keputusan yang lebih efektif. Penelitian ini bertujuan untuk menganalisis pengaruh data analytics terhadap pengembangan pemasaran, khususnya dalam konteks usaha mikro, kecil, dan menengah (UMKM) di Indonesia. Metode yang digunakan adalah studi literatur dengan membandingkan fenomena empiris dan pendekatan teoretis dari berbagai penelitian terdahulu. Hasil kajian menunjukkan bahwa data analytics berperan signifikan dalam meningkatkan efektivitas segmentasi, targeting, dan positioning (STP), sekaligus memperkuat customer engagement dan brand awareness melalui personalisasi serta interaksi digital yang lebih relevan.Selain memberikan peluang strategis, penelitian ini juga menemukan adanya tantangan besar dalam penerapan data analytics di sektor UMKM. Hambatan yang dihadapi meliputi rendahnya literasi digital, keterbatasan infrastruktur, biaya implementasi, serta resistensi budaya organisasi. Faktor-faktor ini memperlihatkan adanya kesenjangan antara teori yang menekankan pentingnya data analytics dengan realitas implementasi di lapangan. Meskipun demikian, dengan dukungan regulasi pemerintah, penguatan literasi digital, serta pendampingan teknis dari berbagai pihak, tantangan ini dapat diatasi sehingga UMKM dapat memaksimalkan manfaat data analytics.Implikasi teoretis dari penelitian ini adalah memperkaya literatur pemasaran digital dengan menegaskan peran data analytics sebagai sumber daya strategis yang mendorong agility dan integrasi omnichannel. Sementara itu, implikasi praktisnya adalah memberikan rekomendasi bagi UMKM untuk menjadikan data analytics sebagai bagian inti dari strategi bisnis dalam menghadapi persaingan global. Penelitian ini menyimpulkan bahwa data analytics bukan sekadar alat analisis, tetapi pilar penting dalam membangun keberlanjutan bisnis dan keunggulan kompetitif di era digital. Keywords: Data analytics, pemasaran digital, customer engagement, UMKM.
Analisis Perbandingan Kinerja Algoritma Naïve Bayes Dan KNN Untuk Memprediksi Penyakit Diabetes Osama Maulana Haq; Achmad Ridwan; Taftazani Ghazi Pratama
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1 (2025): Februari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2424

Abstract

Diabetes is a chronic disease affecting various age groups with a risk of fatal complications. Accurate diagnosis is a crucial initial step in management; however, the gradual progression of symptoms often leads to delayed detection. This study compares the accuracy of the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms in predicting diabetes using a dataset from Kaggle. Naïve Bayes was chosen for its ability to handle large datasets, missing values, irrelevant attributes, and noise, while KNN offers simplicity in implementation. The results show that KNN achieves a higher accuracy of 79% compared to Naïve Bayes at 76%. Therefore, KNN is recommended for diabetes prediction based on this dataset.Keywords: Diabetes; Naïve Bayes, K-Nearest Neighbors; Accuracy AbstrakDiabetes merupakan penyakit kronis yang menyerang berbagai usia dengan risiko komplikasi fatal. Diagnosis yang akurat menjadi langkah awal penting untuk pengelolaan, namun gejala yang berkembang perlahan sering menyebabkan keterlambatan deteksi. Penelitian ini membandingkan akurasi algoritma Naïve Bayes dan K-Nearest Neighbors (KNN) dalam memprediksi diabetes menggunakan dataset dari Kaggle. Naïve Bayes dipilih karena kemampuannya menangani dataset besar, data hilang, atribut tidak relevan, dan noise, sedangkan KNN menawarkan kesederhanaan implementasi. Hasil pengujian menunjukkan bahwa KNN memiliki akurasi lebih tinggi sebesar 79% dibandingkan Naïve Bayes yang mencapai 76%. Dengan demikian, KNN lebih direkomendasikan untuk prediksi diabetes berdasarkan dataset ini.Kata Kunci: Diabetes; Naïve Bayes; K-Nearest Neighbors; Akurasi
On-Time Student Graduation Prediction Modeling: A Comparative Analysis of Naive Bayes Algorithm and Other Data Mining Classifications: Pemodelan Prediksi Kelulusan Mahasiswa Tepat Waktu: Analisis Komparatif Algoritma Naive Bayes Dan Klasifikasi Data Mining Lainnya Achmad Ridwan; Tole Sutikno; Imam Riyadi; Widya Cholid Wahyudin
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i2.1679

Abstract

Predicting the on-time graduation of university students is a crucial task in higher education institutions, enabling proactive support and improving institutional effectiveness. This paper presents a comparative analysis of several machine learning algorithms for predicting on-time graduation, with a specific focus on challenging the performance of the Naive Bayes (NB) algorithm. Although often used as a baseline model, the effectiveness of NB in the complex domain of educational data is frequently debated. We compare NB with MultinomialNB and Decision Tree (DT), both widely favored in recent literature. Using a public dataset containing students' academic records, we follow the CRISP-DM methodology, incorporating feature selection and SMOTE to address class imbalance. The models are evaluated using accuracy, precision, recall, and F1-score metrics. Our results show that while Decision Tree achieves the highest accuracy, Naive Bayes offers an appealing balance of performance, computational efficiency, and interpretability, making it a strong candidate for implementation in early warning systems at universities. This study provides empirical evidence on the role of Naive Bayes in the current landscape of educational data mining. The classification results show an accuracy of 0.82 for Naive Bayes, 0.81 for MultinomialNB, and 0.85 for Decision Tree.
Use of Blockchain for Data Security in E-Government Systems Achmad Ridwan; Kailie Maharjan; Krim Ulwi
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1624

Abstract

The increasing reliance on digital platforms for public administration has heightened concerns about data security in e-government systems. Cyber threats, unauthorized access, and data breaches pose significant risks to the integrity and confidentiality of sensitive governmental information. Blockchain technology, with its decentralized and tamper-proof nature, offers a promising solution for enhancing data security in e-government systems. This research explores the use of blockchain to safeguard data in e-government platforms, focusing on its potential benefits, challenges, and implementation strategies. The study adopts a mixed-method approach, combining a systematic literature review and expert interviews. The literature review analyzed 50 academic articles and industry reports, while interviews with 10 blockchain experts provided practical insights. Key factors such as data integrity, transparency, and access control were evaluated to determine blockchain’s effectiveness in addressing e-government security challenges. The findings reveal that blockchain significantly improves data security by ensuring immutability, enabling secure data sharing, and reducing reliance on central authorities. Experts highlighted blockchain’s potential to enhance transparency and accountability while maintaining privacy through cryptographic techniques. However, challenges such as high implementation costs, scalability issues, and regulatory uncertainties were identified as barriers to adoption. The study concludes that blockchain can revolutionize e-government data security by offering a robust and decentralized framework. Addressing the challenges of implementation and policy alignment will be critical for realizing its full potential. Future research should focus on pilot projects and sector-specific adaptations to accelerate blockchain adoption in e-government systems.
MOBILE APPLICATION DESIGN BASED ON NATURAL LANGUAGE PROCESSING TO IMPROVE THE QUALITY OF HEALTH SERVICES Achmad Ridwan; Zain Nizam; Daniyar Satybaldy
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1626

Abstract

The increasing demand for efficient and personalized health services has driven the integration of advanced technologies into healthcare systems. Mobile applications leveraging natural language processing (NLP) offer promising solutions to improve patient communication, diagnostic accuracy, and service delivery. Despite advancements, challenges remain in developing user-friendly applications that address diverse healthcare needs. This research focuses on designing a mobile application based on NLP to enhance the quality of health services, emphasizing usability, accuracy, and accessibility. The study employs a user-centered design approach combined with experimental evaluation. The application was developed using Python-based NLP libraries, integrating features such as symptom analysis, medical query responses, and appointment scheduling. A prototype was tested with 150 participants, including patients and healthcare professionals, to evaluate performance metrics such as response accuracy, user satisfaction, and system reliability. The findings indicate that the NLP-based application achieved an 85% accuracy rate in interpreting medical queries and a 90% user satisfaction rate. Participants reported improved communication with healthcare providers and faster access to relevant medical information. However, challenges such as handling complex medical terminology and ensuring data privacy were noted. The study concludes that NLP-powered mobile applications have significant potential to improve health service quality by enabling efficient and accurate communication between patients and providers. Addressing challenges related to data security and expanding linguistic capabilities will be essential for future development. The research underscores the importance of integrating advanced technologies to meet the evolving needs of the healthcare sector.
RANCANG BANGUN SISTEM E-VOTING MENGGUNAKAN QR CODE BERBASIS WEBSITE UNTUK PEMILIHAN KETUA HIMPUNAN MAHASISWA SISTEM INFORMASI UNIVERSITAS MUHAMMADIYAH KUDUS dahlan alan; Achmad Ridwan; Soma Setiawan Ponco Nugroho
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10732

Abstract

Penelitian ini dilaksanakan untuk merancang dan membangun sistem E-Voting berbasis website dengan memanfaatkan QR Code sebagai media autentikasi pada pemilihan Ketua Himpunan Mahasiswa Sistem Informasi Universitas Muhammadiyah Kudus. Penelitian ini dilatarbelakangi oleh proses pemilihan yang sebelumnya masih menggunakan Google Form, sehingga dinilai kurang optimal dari segi keamanan, efisiensi, dan transparansi. Pengembangan sistem dilakukan menggunakan metode Waterfall yang meliputi tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Sistem dibangun menggunakan framework CodeIgniter dengan bahasa pemrograman PHP dan database MySQL. Penerapan QR Code bertujuan untuk memastikan setiap pemilih hanya dapat menggunakan hak pilihnya satu kali sehingga dapat mengurangi potensi terjadinya kecurangan. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu membantu panitia dalam mengelola data pemilihan, data kandidat, data pemilih, proses autentikasi, pelaksanaan pemungutan suara, serta penyajian hasil pemilihan secara otomatis dan real-time. Berdasarkan hasil pengujian, seluruh fungsi sistem berjalan sesuai dengan kebutuhan yang telah dirancang sehingga sistem layak digunakan sebagai media pemilihan Ketua Himpunan Mahasiswa Sistem Informasi Universitas Muhammadiyah Kudus.
Co-Authors Ade Ima Afifa Himayati Afina Mahmudah Agung Prihandono Agung, Eka Adnan Andi Kurniawan Aqfianti Fadilatunnisa Aulia Nur Mustaanah dahlan alan Daniyar Satybaldy Dhina Cahya Rohim Diana Tri Lestari Elfanesya Silva Fida Maisa Hana Fitriana Kartikasari Imam Riyadi indah puspitasari Indanah Janu Arlinwibowo Kailie Maharjan Krim Ulwi Loso Judijanto Muhammad Adib Jauhari Muhammad Nurul Fadel Nadia Dwi Irmadiani Nauval Alfarizi Noor Hidayah Osama Maulana Haq Safira Selly Saiful Ulya Sakila Jauza Adiba Siti Sofia Rahma Soma Setiawan P.N Soma Setiawan Ponco Nugroho Sugesti, Edwin Sukesih Sukesih Taftazani Ghazi Pratama Tole Sutikno Widya Cholid Widya Cholid Wahyudin Yayuk Mundriyastutik Yunus Mustaqim Zain Nizam