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Hasbi Firmansyah
Universitas Pancasakti Tegal
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Analisis Kinerja Algoritma K-Nearest Neighbor (KNN) pada Klasifikasi Data Bank Marketing Yosephus Arpan Polado Sinurat; Hasbi Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Intelek Insan Cendikia Vol. 3 No. 1 (2026): JANUARI 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Pemasaran langsung (direct marketing) merupakan salah satu strategi utama industri perbankan untuk menawarkan produk deposito berjangka. Namun, kampanye yang tidak tertarget seringkali tidak efisien dan memakan biaya tinggi. Penelitian ini bertujuan untuk membangun model prediksi klasifikasi menggunakan algoritma K-Nearest Neighbor (KNN) untuk menentukan nasabah yang berpotensi berlangganan deposito berjangka berdasarkan data historis kampanye pemasaran bank. Dataset yang digunakan adalah Bank Marketing Dataset dari UCI Machine Learning Repository. Proses penelitian meliputi pra-pemrosesan data (cleaning, encoding, dan normalisasi Min-Max), pembagian data latih dan uji, serta pengujian nilai $k$ yang berbeda (k=3, 5, 7, 9). Hasil eksperimen menunjukkan bahwa algoritma KNN dengan nilai k=5 menghasilkan kinerja optimal dengan akurasi sebesar 89,2%, presisi 65%, dan recall 58%. Penelitian ini menyimpulkan bahwa KNN efektif digunakan untuk klasifikasi data pemasaran bank, namun memerlukan penanganan ketidakseimbangan kelas untuk meningkatkan nilai recall.
Analisis Pemetaan Pola Pendonor Darah pada Blood Transfusion Service Center Menggunakan Metode Self-Organizing Map Rafli Juan Lauda Al Faiq; Hasbi Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Intelek Insan Cendikia Vol. 3 No. 1 (2026): JANUARI 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Manajemen stok darah sangat bergantung pada perilaku orang yang memberi darah. Penelitian ini bertujuan untuk memahami pola cara orang-orang tersebut memberi darah menggunakan algoritma yang disebut Self-Organizing Map (SOM). Data yang digunakan terdiri dari 748 orang dengan fitur utama berdasarkan model RFM, yaitu tingkat kebaruannya, frekuensi, nilai kontribusi, dan waktu. Dengan metode SOM, data yang memiliki banyak dimensi dipetakan ke dalam grid dua dimensi untuk mengelompokkan orang yang memberi darah berdasarkan tingkat kesetiaannya. Hasil penelitian menunjukkan bahwa pemetaan ini dapat membedakan secara visual antara orang yang aktif dan tidak aktif dalam memberi darah, yang membantu pusat transfusi darah dalam mengambil keputusan yang lebih baik untuk menahan orang-orang yang memberi darah.
Analisis Pengaruh Chat GPT Terhadap Kemampuan Berpikir Kritis Mahasiswa Universitas Pancasakti Tegal Menggunakan Algoritma Regresi Linear Akhmad Ilham Muzaki; Hasbi Firmansyah
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Penelitian ini bertujuan menganalisis pengaruh penggunaan Chat GPT terhadap kemampuan berpikir kritis mahasiswa Universitas Pancasakti Tegal. Penelitian ini menggunakan metode kuantitatif dengan analisis Regresi Linear. Data dikumpulkan melalui kuisioner yang diisi oleh 34 responden. Hasil penelitian ini diharapkan dapat memberikan gambaran tentang pengaruh teknologi kecerdasan buatan terhadap kemampuan berpikir kritis mahasiswa, serta memberikan rekomendasi bagi pengembangan strategi pembelajaran yang efektif. Penelitian ini memiliki implikasi penting bagi pendidikan tinggi dan pengembangan kemampuan berpikir kritis mahasiswa.
Klasifikasi Data Instagram dengan Support Vector Machine Raditya Firmansyah; Hasbi Firmansyah
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Penelitian ini bertujuan untuk mengklasifikasikan konten Instagram menggunakan metode Support Vector Machine (SVM). Dengan semakin meningkatnya jumlah pengguna Instagram, penting untuk mengelompokkan postingan berdasarkan komentar yang diterima. Metode SVM dipilih karena kemampuannya dalam klasifikasi dan akurasi yang tinggi, dengan hasil pengujian mencapai 96% menggunakan data latih dan uji. Proses dilakukan melalui RapidMiner, yang memfasilitasi analisis data mining secara efisien. Hasil penelitian menunjukkan bahwa SVM efektif dalam mengklasifikasikan data komentar Instagram, memberikan wawasan baru dalam pengelolaan konten media sosial.
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.
Evaluasi Performa Algoritma FP-Growth Berdasarkan Variasi Parameter Minimum Support dan Confidence pada Dataset Groceries Arumeilia; Hasbi Firmansyah; Wahyu Arsiyani
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.410

Abstract

This research investigates the relationship patterns among products in the Groceries dataset by applying the FP-Growth algorithm as an approach to uncover association rules. The analysis was conducted by varying the values of minimum support and minimum confidence to observe how these parameters influence the number and quality of generated rules. The experimental findings reveal that the combination of a support value of 0.01 and a confidence value of 0.4 generated the largest number of rules, totaling 71, with the highest lift value reaching 2.344. These results indicate a strong association between several products that frequently appear together within a single transaction, where whole milk emerges as the most dominant item, both as an antecedent and as a consequent. A high lift value suggests that customers who purchase whole milk are more likely to buy related items such as yogurt, curd, or cream cheese. The insights from this study can serve as a valuable reference for retailers in designing more effective product placement, improving promotional strategies, and supporting data-driven business decisions, particularly in cross-selling and inventory optimization.
Evaluasi Klasifikasi Akurasi dan Weighted Mean Precision pada Gradient Boosted Trees untuk Risiko Diabetes Awal Ihya Bahrul Alam; Hasbi Firmansyah; Wahyu Asriyani
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.423

Abstract

Diabetes mellitus is a chronic disease with a high prevalence that requires early‑stage risk detection to enable effective prevention efforts. This study aims to analyze the capability of the Gradient Boosted Trees algorithm to classify early‑stage diabetes risk based on clinical symptoms using the Early Stage Diabetes Risk Prediction dataset. The research methodology includes data preprocessing, splitting the data into training and test sets, and training a Gradient Boosted Trees classification model in RapidMiner with the class attribute set as the labeled target. Model performance is evaluated using accuracy, weighted mean precision, and weighted mean recall metrics to assess the balanced classification ability for each class. Experimental results show that the Gradient Boosted Trees model achieves good classification performance with an accuracy of 91.76%, a weighted mean precision of 92.04%, and a weighted mean recall of 90.49% on the test data, supported by a confusion matrix pattern dominated by correct predictions for both classes. These findings indicate that the Gradient Boosted Trees approach has strong potential to be used as a decision‑support component in early diabetes risk detection systems and is worth further development for broader clinical data scenarios.
Penerapan Algoritma Naive Bayes untuk Memprediksi Keputusan Berlangganan Deposito Berjangka pada Kampanye Pemasaran Langsung Faizal Izma; Hasbi Firmansyah
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.427

Abstract

Direct marketing campaigns via telephone calls are a key strategy for banks to offer term deposit products. However, the effectiveness of this strategy is often hindered by the uncertainty of customer responses. This study aims to predict customer decisions in subscribing to term deposits by utilizing data mining techniques. The data used is sourced from the UCI Machine Learning Repository which is multivariate, covering demographic attributes, financial history, and campaign interactions. Through data pre-processing stages to handle missing values and class imbalance, this study applies classification models to map potential customer patterns. Experimental results show that the classification model is able to predict non-subscribing customers very well (92.67% precision), but still faces challenges in detecting subscribing customers (35.00% precision). These findings indicate that while the model can help filter marketing targets, further optimization is needed to address data imbalance to improve prediction accuracy in the minority class.
Co-Authors . Eko Budiraharjo Akhmad Ilham Muzaki Ali Sofyan Arumeilia Asriyani, Wahyu Chandra Pratama Putra Raharja Eko Budi Raharjo Faizal Izma Fasya Gilar Amali Hesti Ifada Mustio Ihya Bahrul Alam Lucky Primanda Mohamad Faris Fadil Muhamad Aris Purwanto Muhammad Azfa Al Faruq N Tahta Phudjashakty Nadya Permata Sari Panji Pangestu Saputra Priyo Haryoko Raditya Firmansyah Rafli Juan Lauda Al Faiq Rangga Saputra Ria Indah Fitria Rizki Prasetyo Tulodo Sulistyaningrum Sulistyaningrum Wahyu Arsiyani Wahyu Asriani Wildani Eko Nugroho Yosephus Arpan Polado Sinurat