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INDONESIA
Journal of Computing and Informatics Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2808375X     DOI : -
Core Subject : Science,
Computer Science & IT
Fokus kajian Journal of Computing and Informatics Research mempublikasikan hasil-hasil penelitian pada bidang informatika, namun tidak terbatas pada bidang ilmu komputer yang lain, seperti: 1. Kriptografi, 2. Artificial Intelligence, 3. Expert System, 4. Decision Support System, 5. Data Mining, dan lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 100 Documents
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The Combination of WENSLO and MUNRA Method in Selecting the Best Employees Based on Multiple Criteria Junhai Wang; Setiawansyah Setiawansyah; Adhie Thyo Priandika; Dedi Darwis; Ari Sulistiyawati
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2472

Abstract

This study examines the application of a combination of the WENSLO and MUNRA methods in selecting the best employees based on various criteria to address issues of subjectivity and instability in employee rankings that often arise when data is heterogeneous and criteria are conflicting. The WENSLO method is used to assess and prioritize criteria through structured and preference-based weighting, while MUNRA plays a role in consistently normalizing data and calculating weighted scores for each alternative. The integration of these two methods allows for a more objective evaluation, reduces subjective bias, and produces stable employee rankings even in the presence of data variations or conflicting criteria. The Employee Ranking results show that the top-performing employee is Lestari with a score of 1.3092, followed by Susilo with a score of 1.3080 and Maharani with a score of 1.3003, indicating superior and relatively balanced performance. These findings confirm that the combination of WENSLO and MUNRA can produce clear, objective, and effective employee rankings, as well as provide an adaptive framework to support strategic human resource management.
Optimasi Seleksi Fitur Adaptive Particle Swarm Optimization Untuk Klasifikasi Penyakit Jantung Dengan Ensemble Learning Bagas Adi Nata; Solikhun Solikhun
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2630

Abstract

Heart disease classification using machine learning requires relevant features and predictive models capable of consistently generalizing clinical patterns. Previous studies on the Heart Failure Prediction dataset demonstrated that K-Nearest Neighbor (KNN) optimized with Particle Swarm Optimization (PSO) achieved an accuracy of 89.09% and an Area Under the Curve (AUC) of 0.935. However, the use of a fixed inertia weight and reliance on a single learner may limit the balance between exploration and exploitation, thereby reducing model robustness. This study proposes a feature selection approach based on Adaptive Particle Swarm Optimization (APSO), in which the inertia weight is gradually decreased from 0.90 to approximately 0.42 over 30 iterations. The optimal feature subset is subsequently utilized in a soft voting ensemble learning model. The dataset consists of 918 records, 11 predictive features, and one target class (HeartDisease). Experimental results indicate that the proposed APSO-based ensemble model achieved an accuracy of 89.71%, an F1-score of 0.8986, and an AUC of 0.9466. The confusion matrix yielded 90 true negatives, 12 false positives, 9 false negatives, and 93 true positives on 204 testing instances. Compared with the baseline KNN-PSO model, the proposed method improved classification accuracy by 0.62 percentage points and increased the AUC by 0.0116, while maintaining a disease-class recall of 91.18%. These findings demonstrate that combining adaptive search dynamics with heterogeneous ensemble learning enhances the discriminative capability of heart disease classification, although further validation using identical data partitioning strategies and external datasets is still required
Model Hybrid CNN Mengintegrasikan NasNetMobile dan MobileNet untuk Meningkatkan Akurasi Klasifikasi White Blood Cell Sandi Putra Siregar; Anjar Wanto; Sundari Retno Andani
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2635

Abstract

Sel darah putih merupakan komponen vital dalam sistem kekebalan tubuh pada manusia yang berperan penting dalam melindungi tubuh dari serangan mikroorganisme penyebab penyakit. Variabilitas hasil dalam klasifikasi sel darah putih yang disebabkan oleh keterbatasan metode identifikasi manual masih menjadi isu kritis bagi akurasi system diagnostic berbasis citra. Dalam studi ini difokuskan untuk mengatasi permasalahan tersebut dengan merancang model jarignan saraf konvolusional (CNN) hybrid baru yang dinamakan SAN-Net, yang mengintegrasikan keunggulan arsitektur NASNetMobile dan MobileNet guna meningkatkan akurasi dalam klasifikasi jenis sel darah putih (basophil, erythroblast, monocyte, myeloblast, dan seg neutrophil). Model yang diusulkan dilatih menggunakan dataset citra sel darah putih yang dikumpulkan dari Kaggle kemudian dibandingakan dengan arsitektur standar yakni NASNetMobile. Hasil Pengujian menunjukkan bahwa model SAN-Net memberikan performa terbaik, dengan capaian akurasi, presisi, recall, dan Skor F1 sebesar 99,80%, serta secara signifikasi melampaui kinerja model pembanding. Temuan ini mengindikasikan bahwa potensi arsitektur deep learning modern dalam menghadirkan sistem klasifikasi sel darah putih otomatis dengan konsisten dan akurat, sehingga dapat meningkatkan efisiensi proses diagnosis.
Sistem Pendukung Keputusan Pemilihan Merek Body Lotion Lokal Terbaik untuk Mencerahkan Kulit dengan Menggunakan Metode MAUT Nurul Aisyah; Selly Andari; Ririn Nadya Utari; Nadya; Dedy Hartama; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2636

Abstract

This study aims to determine the best local body lotion brand for skin brightening using the Multi-Attribute Utility Theory (MAUT) method. MAUT was chosen for its capability to process data based on various criteria such as benefits, quality, effectiveness, price, and brand reputation. Data were collected through online questionnaires distributed via Google Forms and shared on social media. From 36 alternatives, five local brands were selected for analysis: Marina, Citra, Scarlett, Natur-e, and Herborist. The analysis process involved determining the weight of each criterion, matrix normalization, utility evaluation, and alternative ranking. The results indicate that Marina ranks first with a score of 19, followed by Scarlett (8.7) and Citra (8.1). The MAUT method has proven effective in supporting decisions regarding the selection of the best local body lotion brand, providing objective and structured guidance for consumers.
Klasifikasi Penyakit Buah Jambu Menggunakan Model MobileNetV2 Berbasis Convolutional Neural Network Anan Wibowo; Agus Perdana Windarto; Poningsih; Rafika Dewi
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2637

Abstract

Pengenalan penyakit pada buah jambu (Psidium guajava) secara dini merupakan langkah penting untuk menjaga kualitas hasil panen dan mencegah kerugian ekonomi akibat serangan penyakit tanaman. Penelitian ini bertujuan untuk mengklasifikasikan penyakit buah jambu berdasarkan citra digital menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Dataset yang digunakan terdiri atas tiga kelas, yaitu Anthracnose, Fruit Fly, dan Healthy Guava. Proses penelitian meliputi tahap data preprocessing, augmentasi citra, pembagian dataset menjadi data latih, validasi, dan uji, serta pelatihan model menggunakan transfer learning dari bobot awal ImageNet. Hasil pengujian menunjukkan bahwa model MobileNetV2 mampu mencapai akurasi sebesar 93% dengan nilai precision rata-rata 0.94, recall 0.93, dan F1-score 0.93. Hasil ini menunjukkan bahwa arsitektur MobileNetV2 efektif dalam mengidentifikasi penyakit buah jambu dengan efisiensi komputasi yang tinggi. Penelitian ini diharapkan dapat mendukung pengembangan sistem deteksi otomatis berbasis visi komputer di bidang pertanian digital.
Sistem Pendukung Keputusan Pendataan Warga Penerima Bantuan Raskin dengan Menerapkan Metode Weight Aggregated Sum Product Assesment (WASPAS) Mesran Mesran; Rosmita Sari; Ridha Maya Faza Lubis; Muhammad Syahrizal
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2642

Abstract

Raskin (rice for the poor) is a rice program for the poor. The Raskin program is one of the government's efforts to reduce the burden of expenditure on poor families. However, in practice, decision-making for determining the criteria for rice recipients usually does not refer to the criteria of poor families, resulting in misdirected rice distribution. To address this issue, a decision support system will be developed to assist in the targeted distribution of Raskin using the Weighted Aggregated Sum Product Assessment (WASPAS) method. This research was conducted by finding the weight value for each attribute, then a ranking process was carried out to determine the best alternative. The criteria used were: Type of Employment, Income, House Condition, Family Size, Age. The results of the study recommend that alternative 4, with the highest score of 0.676, be selected to receive Raskin assistance
Analisis Kondisi Sosial Ekonomi Santri Menggunakan Metode K-Means Clustering Putri Nuraini Qolbiati; Irfan Pratama
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i3.3072

Abstract

Abstrak− Analisis terhadap kondisi sosial santri di MI Al Huda Kota Malang disusun berdasarkan sejumlah indikator penting yang meliputi tingkatpenghasilan, jenis pekerjaan, latar belakang pendidikan, serta kepemilikantempat tinggal. Ketiadaan sistem klasifikasi yang terorganisir menyebabkanproses penentuan tingkat kesejahteraan belum dapat dilakukan secara objektifdan terukur. Untuk mengatasi hal tersebut, digunakan pendekatan data miningmelalui penerapan algoritma K-Means Clustering sebagai metode pengelompokan data. Tahapan awal dilakukan dengan proses preprocessing data, yang mencakup pemilihan variabel yang relevan, penanganan data, sertanormalisasi menggunakan StandardScaler agar distribusi data menjadi lebihseragam. Setelah itu, evaluasi terhadap hasil clustering dilakukan denganmemanfaatkan beberapa metrik, yaitu Elbow Method, Silhouette Score, dan Davies-Bouldin Index guna menentukan jumlah cluster yang optimal.Berdasarkan hasil evaluasi, diperoleh jumlah cluster terbaik sebanyak empat (k = 4), dengan nilai Silhouette Score sebesar 0,3341 dan Davies-Bouldin Indexsebesar 1,1277. Setiap cluster kemudian dianalisis lebih lanjut sesuaikarakteristik masing-masing tanpa melakukan perubahan terhadap jumlahcluster yang telah ditetapkan. Sebagai perbandingan, algoritma DBSCAN juga diterapkan, namun menunjukkan performa yang lebih rendah dibandingkandengan K-Means dalam menghasilkan kualitas pengelompokan. Secarakeseluruhan, metode K-Means terbukti mampu memberikan hasil clusteringyang lebih konsisten dan representatif, sehingga dapat dimanfaatkan sebagai dasar dalam pengambilan keputusan terkait kondisi sosial ekonomi santri.
Signature Identification Based on GLCM Feature Extraction and Convolutional Neural Network Classification Lailan Sofinah Harahap; Haliza Suci Rachmadini
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i3.3078

Abstract

Signature is one of the widely used biometric features for authentication and identity verification purposes. This study proposes a digital signature identification system by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction and Convolutional Neural Network (CNN) classification. The dataset consists of 500 signature images from 50 individuals collected independently. Preprocessing steps include grayscale conversion, adaptive binarization, and image normalization to 128×128 pixels. GLCM texture features are extracted at four angular directions (0°, 45°, 90°, 135°) yielding five main features: contrast, correlation, energy, homogeneity, and entropy. These features are integrated as additional inputs to the fully connected layer of a CNN comprising three convolutional blocks. Experimental results demonstrate that the proposed system achieves a classification accuracy of 96.8%, precision of 96.2%, and F1-Score of 96.5% on test data. These results confirm that integrating GLCM texture features into the CNN architecture significantly improves signature identification performance.
Segmentasi Pelanggan E-Commerce Berbasis Perilaku Belanja Menggunakan Algoritma K-Means Clustering dan Evaluasi Silhouette Score Muhammad Yusran; Rizkah Fadillah; Mesran; Raymond Shawn
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i3.3079

Abstract

Dalam era persaingan E-Commerce yang ketat, pemahaman mendalam mengenai perilaku konsumen menjadi kunci utama keberhasilan strategi pemasaran. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan toko online menggunakan algoritma K-Means Clustering berbasis perilaku belanja. Menggunakan dataset Mall Customer Segmentation, penelitian ini memproses fitur utama berupa Pendapatan Tahunan dan Skor Pengeluaran yang dinormalisasi. Evaluasi model dilakukan menggunakan metode Elbow dan Silhouette Score untuk menentukan jumlah kelompok optimal. Hasil penelitian menunjukkan bahwa pembagian pelanggan menjadi 5 cluster adalah yang paling optimal dengan nilai Silhouette Score sebesar 0.5547. Kelima segmen yang terbentuk meliputi Middle Class (40,5%), VIP/Big Spenders (19,5%), Hemat Mapan (17,5%), Ekonomis (11,5%), dan Impulsif (11%). Temuan ini memberikan wawasan strategis bagi pelaku bisnis untuk merancang kampanye pemasaran yang terpersonalisasi, seperti program loyalitas eksklusif untuk segmen VIP dan penawaran diskon intensif untuk segmen Ekonomis, guna meningkatkan retensi dan profitabilitas
Sistem Pendukung Keputusan Penerima BANPRES UMKM Penanganan Covid-19 Menerapkan Metode WASPAS Mochamad Dedy Subekti Rahardjo; Amelia Belinda Silviana; Dini Andriyani; Agus Turiyono
Journal of Computing and Informatics Research Vol 5 No 3 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i3.3119

Abstract

The Covid-19 pandemic has had a significant impact on the sustainability of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. To assist affected MSMEs, the government launched the Presidential Assistance for Micro Business Productiveness (BANPRES UMKM) program. However, in its implementation, the process of determining aid recipients often faces obstacles such as inaccurate targeting, lack of objectivity, and time constraints in the selection process. Therefore, a system is needed that can assist in effective and efficient decision-making. This study aims to design a Decision Support System (DSS) to determine BANPRES UMKM recipients by applying the Weighted Aggregated Sum Product Assessment (WASPAS) method. The WASPAS method was chosen because it is able to integrate the advantages of the Weighted Sum Model (WSM) and Weighted Product Model (WPM) methods, resulting in more stable and accurate calculations in multi-criteria problems. The criteria used in this system include business legality, length of business operation, level of losses due to the pandemic, number of family dependents, and asset ownership. System testing results demonstrate that the WASPAS method is capable of objectively ranking alternatives, thus supporting a more transparent and targeted selection process. This research is expected to provide a technological solution to support data-driven social assistance distribution and measurable decision-making logic.

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