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All Journal JURNAL PENDIDIKAN TAMBUSAI PHILOSOPHICA Jurnal Bahasa, Sastra, dan Budaya Jurnal Pendidikan dan Konseling Al Maktabah Jurnal kajian Ilmu dan Perpustakaan Sasando : Jurnal Bahasa, Sastra Indonesia, dan Pengajarannya Jurnal Ilmiah SEMANTIKA Alinea: Jurnal Bahasa, Sastra, dan Pengajaran Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Innovative and Creativity Jurnal Intelek Dan Cendikiawan Nusantara Jurnal Intelek Insan Cendikia Jurnal Pustaka Cendekia Hukum dan Ilmu Sosial
Asriyani, Wahyu
Universitas Pancasakti Tegal
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Prediksi Unit Price Properti Menggunakan Algoritma Neural Network Berbasis RapidMiner: Penelitian Bimo Aryo Pangestu; Hasbi Firmansyah; Ali Sofyan; Wahyu Asriyani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.4439

Abstract

This study aims to predict property unit price using the Neural Network algorithm based on RapidMiner. The dataset used consists of property-related attributes, with unit price as the target variable. The research stages include attribute role assignment, data normalization, and data partitioning using the estimation method with a 70:30 split between training and testing data. The Neural Network model is built using the training data and applied to the testing data to generate unit price predictions. Model performance is evaluated using the Performance (Regression) method with the Root Mean Squared Error (RMSE) metric. The experimental results show that the Neural Network algorithm is able to predict property unit price accurately, as indicated by an RMSE value of 0.028. The low RMSE value indicates a small difference between the actual and predicted unit price values, demonstrating that the proposed model has good predictive performance. Therefore, it can be concluded that the Neural Network algorithm based on RapidMiner is effective for predicting property unit priprice and can be used as an alternative approach in property price analysis.
Analisis Pengaruh Parameter Support Vector Machine Terhadap Akurasi Prediksi Harga Saham: Penelitian Arief Priyono; Hasby Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4529

Abstract

Stock price prediction is challenging due to fluctuating and nonlinear behavior. This study examines the effect of parameter optimization in Support Vector Machine (SVM) on prediction accuracy and error for stock prices. The dataset consists of PT Telekomunikasi Indonesia Tbk (TLKM) stock data from 2022–2024 obtained from Yahoo Finance. The workflow includes normalization, windowing-based feature construction, train–test splitting, and modeling using ε-Support Vector Regression (ε-SVR) with a Radial Basis Function (RBF) kernel. Parameter optimization is conducted via Optimize Parameters (Evolutionary) to find suitable C, gamma, and epsilon values, and the optimized model is compared against a baseline using LibSVM default parameters. Performance is evaluated using Root Mean Squared Error (RMSE), Absolute Error (AE), Correlation, and Prediction Average. Results indicate that the optimized model produces more stable predictions and follows the actual pattern more consistently, although the baseline may yield lower numerical error in some cases. This finding suggests that parameter optimization increases model sensitivity to training patterns but requires careful regularization to prevent accuracy degradation on test data.
Penerapan Algoritma k-Nearest Neighbor untuk Klasifikasi Kondisi Lingkungan Pertanian Berbasis IoT : Penelitian Panji Pangestu Saputra; Hasbi Firmansyah; Rizki Prasetyo Tulodo; Priyo Haryoko; Wahyu Asriyani
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4566

Abstract

The development of the Internet of Things (IoT) has encouraged the adoption of smart technologies in agriculture to enable real-time environmental monitoring. This study aims to apply the k-Nearest Neighbor (k-NN) algorithm to classify agricultural environmental conditions into ideal and non-ideal categories based on IoT sensor data. The dataset used in this research was obtained from an open-source repository and consists of several environmental parameters, including temperature, humidity, and soil moisture. The research stages include data preprocessing, attribute and label determination, data normalization using the z-transformation method, and model evaluation through cross validation. The performance of the classification model was assessed using accuracy, precision, recall, and F-measure metrics. The experimental results indicate that the k-NN algorithm is capable of providing good classification performance in identifying agricultural environmental conditions. However, limitations were observed in detecting minority class instances, suggesting the need for further parameter optimization and model enhancement. This research is expected to serve as a foundation for the development of IoT-based smart agriculture systems to support more effective decision-making in agricultural environmental management.
Segmentasi Pelanggan Grosir Menggunakan K-Means: Analisis Outlier dan Ketidakseimbangan Data : Penelitian N Tahta Phudjashakty; Hasbi Firmansyah; Wahyu Asriyani; Ali Sofyan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4771

Abstract

This study aims to segment wholesale customers using the K-Means clustering algorithm and to examine the impact of outliers and data imbalance on the clustering results. The data are taken from the Wholesale Customers Dataset of the UCI Machine Learning Repository, consisting of 440 customers with eight numerical attributes representing annual purchase amounts. The preprocessing steps include exploratory data analysis, outlier detection using Z-Score and boxplot visualization, handling of extreme values with winsorizing, and Z-Score normalization to make the attribute scales comparable. The number of clusters is determined using the Elbow Method. Applying K-Means with produces two highly imbalanced clusters, with 437 customers in Cluster 0 and 3 customers in Cluster 1. Cluster 0 represents regular customers whose purchasing patterns are close to the overall average, while Cluster 1 consists of customers with very high purchases, especially in Frozen and Delicassen categories. Evaluation using the average within centroid distance and the Davies–Bouldin Index shows that, after outlier handling and normalization, the cluster structure becomes more stable and easier to interpret. The resulting segmentation can support differentiated marketing and service strategies for regular and high-spending customers and highlights the importance of proper preprocessing when applying K-Means.
Penerapan Algoritma Logistic Regression untuk Memprediksi Keberhasilan Terapi Kutil (Cryotherapy) Muhammad Fiqih Ainurohman; Hasbi Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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

Abstract

Cryotherapy (terapi beku) adalah cara umum untuk mengobati kutil, tetapi tingkat keberhasilannya berbeda-beda antar pasien. Penelitian ini bertujuan membuat model untuk memprediksi apakah Cryotherapy berhasil atau tidak, menggunakan teknik Data Mining dengan algoritma Logistic Regression. Data yang digunakan berasal dari UCI Machine Learning Repository, yang berisi 90 data pasien dan 6 informasi klinis, yaitu usia, jenis kelamin, durasi terapi, jumlah kutil, jenis kutil, dan luas area kutil. Penelitian dilakukan dengan menggunakan perangkat lunak RapidMiner dan metode validasi 10-Fold Cross Validation. Hasil menunjukkan algoritma Regresi Logistik mampu memprediksi keberhasilan terapi dengan akurasi sebesar 86,67%. Penelitian juga menemukan bahwa variabel usia pasien yang muda, yaitu antara 15 sampai 20 tahun, berpengaruh paling besar terhadap hasil pengobatan.
PREDIKSI RISIKO KESEHATAN IBU HAMIL DENGAN MENGGUNAKAN METODE DECISION TREE Raihan Adyatma; Hasbi Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Kesehatan ibu hamil merupakan prioritas utama dalam tujuan pembangunan kesehatan global, mengingat masih tingginya Angka Kematian Ibu . Salah satu penyebab kematian ibu adalah keterlambatan dalam mendeteksi faktor risiko seperti hipertensi dan diabetes gestasional. Penelitian ini bertujuan untuk membangun model prediksi tingkat risiko kesehatan ibu hamil  menggunakan teknik Data Mining dengan algoritma Decision Tree. Data yang digunakan bersumber dari UCI Machine Learning Repository yang terdiri dari 1.014 data rekam medis dengan atribut meliputi usia, tekanan darah, kadar gula darah, suhu tubuh, dan detak jantung. Pengolahan data dilakukan menggunakan perangkat lunak RapidMiner Studio. Hasil penelitian menunjukkan bahwa algoritma  mampu mengklasifikasikan risiko ke dalam tiga kategori (Low, Mid, High Risk) dengan tingkat akurasi sebesar [Akurasi 74.43%]. Berdasarkan struktur pohon keputusan yang terbentuk, atribut kadar gula darah  ditemukan sebagai faktor paling dominan dalam menentukan tingkat risiko. Model ini diharapkan dapat membantu tenaga medis dalam melakukan deteksi dini komplikasi kehamilan.Kata kunci: Klasifikasi Sampah; Convolutional Neural Network (CNN); Deep Learning; Pengelolaan Sampah; Visi Komputer
Co-Authors . Eko Budiraharjo Abrori Musafic Al Rasyid Afsun Aulia Afsun Aulia Nirmala Ahmad Ferdi Dwi Nugroho Ai Djamilatul Wahidah Aifa Inayati Ali Sofyan Amelia Laura Ardianti Aminatuz Zahro Arief Priyono Armand Cahya Nugraha Bimo Aryo Pangestu Burhan Eko Purwanto Chandra Pratama Putra Raharja Devina Anggraeni Eko Budi Raharjo Erza Rifa Nihayah Fadil, Mohamad Faris Fauziah Elis Cholisatul Fifit Solihatun Firmansyah, Hasbi Fuada, Sania Fitra Hanjiyan Riyan Hidayat Hasbi Firmansyah Hasby Firmansyah Inayah, Dwi Nita Laeli Intan Puspita Sari Irfandi Irfandi Khusnul Khotimah Kuncoro Singgih Prasojo Leli Triana Lisa Nur Amelia Lyswidia Andriarsih M.Fery Ardiansyah Mohamad Raffi Iskandar Mohamad Shofy Muhamad Aris Purwanto Muhammad Fiqih Ainurohman Muhammad Hanif Fathurahman Musthofa, Zaeny N Tahta Phudjashakty Panji Pangestu Saputra Priyo Haryoko Purwanto, Burhan Eko Rafli Juan Lauda Al Faiq Raihan Adyatma Ramadhani Zidan Arifin Rawoyo Ria indah fitria Ridlo, Muhammad Rasyid Riski Prasetyo Tulodo Rizki Prasetyo Tulodho Rizki Prasetyo Tulodo Sania Fitra Fuada Saputri, Yoandini Sari, Yulia Prima Sri Mulyati Sri Mulyati SRI UTAMI Sutji Muljani Syamsul Anwar Syamsul Anwar Tri Mulyono Tri Mulyono Widiastuti Widiastuti Winda Gunansi Windi Listiyawati Yoandini Saputri Yosephus Arpan Polado Sinurat Yulia Prima Sari