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All Journal Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) JURIKOM (Jurnal Riset Komputer) JE (Journal of Empowerment) Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Prabowo, Wahyu Aji Eko
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Klasifikasi Penyakit Diabetes Menggunakan Pendekatan Pembelajaran Mesin dengan Model Non-linier Adi, Ilham Arif Kuncoro; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8586

Abstract

The increasing prevalence of diabetes mellitus highlights the need for accurate early detection methods. This study proposes a classification model for diabetes prediction using non-linear machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (K-NN). The dataset, obtained from Kaggle, includes clinical features such as glucose levels, BMI, blood pressure, and insulin. The methodology comprises data preprocessing, partitioning the data into training and testing sets, and evaluating the model’s using accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest algorithm achieved the highest performance, followed by SVM and K-NN. We attribute Random Forest’s superior performance to its robustness in handling complex patterns and minimizing overfitting. We expect this research to contribute to developing practical early detection tools for diabetes, thereby supporting timely and data-driven medical decision-making.
Pemanfaatan Model Linier dalam Klasifikasi Penyakit Diabetes Berbasis Machine Learning Ajisaputra, Faris Prasetya; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8587

Abstract

Diabetes is a chronic disease that may lead to serious health complications if not detected and treated early. Early detection plays a crucial role in minimizing long-term risks. This study aims to classify diabetes cases using a machine-learning approach based on linear models. The models applied in this research include logistic regression, linear discriminant analysis (LDA), ridge classifier, and support vector machine (SVM) with a linear kernel. We preprocessed the dataset to ensure quality and consistency. We evaluated each model’s performance using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results show that the ridge classifier achieved the highest performance, followed by LDA and linear SVM, with comparable results. Logistic regression also performed reasonably well, albeit with slightly lower metrics. These findings indicate that the linear model can provide accurate and reliable classification in the task of predicting diabetes, contributing to the proof that this model can serve as the basis for a decision support system for early diabetes diagnosis in the healthcare sector.
Analisis Performansi Model Machine Learning dalam Klasifikasi Penyakit Diabetes Tipe 2 Hidayatulloh, Ryan; Prabowo, Wahyu Aji Eko
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8747

Abstract

Type 2 Diabetes Mellitus is a chronic disease that develops gradually and can lead to serious complications—such as heart disease, kidney failure, and blindness—if not detected early. This study aims to evaluate and compare the performance of four machine learning algorithms—Logistic Regression, Random Forest, Multilayer Perceptron, and Deep Neural Network—in predicting the risk of type 2 diabetes based on medical data. The analysis uses the Pima Indians Diabetes dataset, which contains 9.538 patient records and 16 predictor variables. We split the data into training and testing sets using an 80:20 ratio. During training, we performed hyperparameter tuning using Grid Search combined with cross-validation. To evaluate model performance, we applied several metrics, including accuracy, precision, recall, F1-score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R², and an analysis of overfitting. The results indicate that the Random Forest model outperformed the others, achieving 100% accuracy, zero classification errors, near-zero prediction error values, and no signs of overfitting. Logistic Regression also performed well, though slightly below the Random Forest. In contrast, the Multilayer Perceptron and Deep Neural Network models showed mild overfitting and higher false negative rates. Based on these findings, we recommend the Random Forest model as the most reliable option for early prediction systems in type 2 diabetes mellitus.
PENGENALAN PERANGKAT LUNAK LaTeX SEBAGAI MEDIA ALTERNATIF PENULISAN BUKU AJAR BAGI GURU Herowati, Wise; Budi, Setyo; Wibawa, Tangkas Surya; Prabowo, Wahyu Aji Eko
JE (Journal of Empowerment) Vol 3, No 2 (2022): DESEMBER
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v3i2.2740

Abstract

ABSTRAK Penulisan dokumen secara digital merupakan kemampuan yang harus dimiliki di era digitalisasi sekarang ini. Secara khusus pada bidang pendidikan, tenaga pendidik wajib memiliki ketrampilan dalam penulisan teks secara digital. Beberapa tahun yang lalu, penulisan dokumen dilakukan menggunakan mesin tik. Kemudian beralih ke era digital,  diperkenalkan perangkat lunak berbasis komputer, salah satunya adalah Microsoft Word. Meskipun penggunaannya yang mudah, namun kurang dapat menampilkan visualisasi persamaan matematika dengan indah. Perangkat lunak lain yang dapat digunakan untuk membuat formulasi matematika lebih rapi dan indah adalah LaTeX. Tujuan dari kegiatan pengabdian kepada masyarakat (PKM) ini adalah untuk memperkenalkan dan memberi pelatihan pengggunaan LaTeX kepada tenaga pendidik di Yayasan Hidayatullah Gunung Pati, Kota Semarang. Metode yang digunakan pada PKM ini adalah dengan melakukan pengenalan dan pelatihan penggunaan perangkat lunak LaTeX. Luaran PKM ini adalah softskill guru meningkat sehingga menunjang proses penulisan buku ataupun media ajar yang lain sehingga kualitas SDM dan Yayasan menjadi lebih baik.ABSTRACTNowadays, the ability to document writing is important. In particular, in the field of education, educators are required to have the ability and soft skills in digital text writing. A few decades ago, writing documents was done using a typewriter. In this digital era, computer-based software was introduced, and one of the software was Microsoft Word. Even though easy to use, it is not able to visualize the mathematical formula beautifully. Another software that can produce the mathematical formula beautifully is LaTeX. The purpose of this PKM is to introduce and train on the use of latex to educators at the Hidayatullah Foundations, Gunung Pati, Semarang City. The method used in this PKM is to introduce and train the use of LaTeX software. The output of this PKM is increasing the educator’s soft skills to support the production of teaching media so that the quality of human resources becomes better. 
Peningkatan Brand UMKM Melalui Pendekatan Konten Animasi Prabowo, Wahyu Aji Eko; Zulfiningrum, Rahmawati; Siregar, Nadia Itona; Nugraini, Siti Hadiati; Ayasy, Ahmad Yahya; Adriansyah, Vicky Puja; Putrawan, Zulhandi
DEDIKASI PKM Vol. 5 No. 3 (2024): DEDIKASI PKM UNPAM
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/dkp.v5i3.40174

Abstract

Potensi wisata terhadap UMKM akan meningkatkan pendapatan wilayah. Salah satu pengembangan daerah wisata yang berpotensi besar dalam meningkatkan perekonomian negara adalah destinasi wisata Karimunjawa. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan brand UMKM melalui pendekatan konten animasi sebagai ikon UMKM di Karimunjawa, bekerjasama dengan BUMDES Sejahtera Bahari. Kegiatan ini mengusung pentingnya ikon bagi UMKM lokal dalam mempromosikan identitas produk mereka. Program ini dirancang untuk memanfaatkan elemen budaya unik yang ada di Karimunjawa, meningkatkan kemampuan lokal dalam pembuatan konten animasi, dan memperkuat pemasaran UMKM. Metode yang digunakan adalah Asset Based Community Development (ABCD) untuk membantu masyarakat dalam melihat potensi yang dimiliki dan mengarahkan untuk peningkatan. Pelatihan dilaksanalan dalam bentuk workshop yang bertujuan untuk merancang pembuatan video animasi sebagai implementasi alat pemasaran yang efektif. Manfaat utama kegiatan ini adalah peningkatan pengetahuan dan keterampilan lokal dalam mengeksplorasi potensi daerah, dengan harapan meningkatkan keterlibatan dan daya ingat konsumen terhadap produk lokal. Melalui video animasi diharapkan dapat membantu promosi produk UMKM Karimunjawa, peningkatan ekonomi bagi masyarakat lokal dan menambah daya tarik wisatawan.
Co-Authors Achmad Wahid Kurniawan Adi, Ilham Arif Kuncoro Adriansyah, Vicky Puja Ajisaputra, Faris Prasetya Akrom, Muhamad Almasah, Muhammad Rakha Ayasy, Ahmad Yahya Ayu Pertiwi Budi, Setyo Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Hidayatulloh, Ryan Moch Anjas Aprihartha Muhammad Naufal, Muhammad Nadia Itona Siregar, Nadia Itona Noor Ageng Setiyanto, Noor Ageng Nugraini, Siti Hadiati Putrawan, Zulhandi Safitri, Aprilyani Nur Sudibyo, Usman Trisnapradika, Gustina Alfa Wibawa, Tangkas Surya Wibawa, Vincentius Hadianta Zulfiningrum, Rahmawati