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All Journal Jurnal Teknik ITS Scientific Journal of Informatics Jurnal Informatika Upgris Insect (Informatics and Security) : Jurnal Teknik Informatika E-Link: Jurnal Teknik Elektro dan Informatika REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Intelligent Decision Support System (IDSS) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Abdi Masyarakat Indonesia Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Jurnal Teknologi Sistem Informasi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer JSITP Journal of Artificial Intelligence and Digital Business Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research SOROT: Jurnal Pengabdian Kepada Masyarakat Jurnal Akademik Pengabdian Masyarakat Kadaster: Journal of Land Information Technology Svarga Pena: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika dan Desain Komunikasi Visual
Bangkit Indarmawan Nugroho
STMIK YMI TEGAL
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Customer segmentation in sales transaction data using k-means clustering algorithm Nugroho, Bangkit Indarmawan; Rafhina, Ana; Ananda, Pingky Septiana; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.236

Abstract

Customer segmentation against sales transaction data using K-Means clustering algorithm. The purpose of this research is to develop and validate a customer segmentation model using an optimized K-Means clustering algorithm to enable more accurate customer grouping based on sales transaction data. The methodology used includes quantitative design combined with experimental techniques, quantitative data analysis, and model validation, where rice sales transaction data from Tegal city traditional market is processed to identify customer segments. The results showed the effectiveness of the optimized K-Means algorithm in grouping customers into three clusters based on purchase characteristics, and C4-SUPER rice proved to be the best-selling among consumers. These insights enable the development of more targeted and personalized marketing strategies, enrich the academic literature on customer data analysis, and move towards the practical application of more effective customer segmentation through the use of advanced analytical technologies
Penerapan Metode Rule Based System Untuk Menentukan Jenis Tanaman Pertanian Berdasarkan Ketinggian Dan Curah Hujan Supratman, Ardhi; Nugroho, Bangkit Indarmawan; Syefudin, Syefudin; Kurniawan, Rifki Dwi
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10235

Abstract

Penelitian ini mengembangkan sebuah metode Rule Based System untuk menentukan jenis tanaman pertanian yang optimal berdasarkan ketinggian dan curah hujan. Dengan menggabungkan data ketinggian dan data curah hujan dari Badan Pusat Statistik (BPS) Kabupaten Tegal, sistem ini menggunakan pengetahuan ahli pertanian untuk menghasilkan rekomendasi tanaman. Implementasi dilakukan dengan menggunakan Python dan framework flask, menyajikan hasil dalam bentuk website. Evaluasi menunjukkan bahwa metode ini efektif dalam menghasilkan rekomendasi tanaman yang sesuai dengan kondisi lingkungan. Meskipun ada beberapa kasus ketidaksesuaian, hasilnya menegaskan potensi metode Rule Based System dalam meningkatkan akurasi pengambilan keputusan pertanian. Penelitian ini memberikan wawasan untuk pengembangan lebih lanjut dengan fokus pada peningkatan keakuratan dan validasi sistem yang lebih komprehensif.
Perbandingan Metode Fuzzy Mamdani dan Fuzzy Tsukamoto untuk Identifikasi Tingkat Serangan Penyakit pada Tanaman Bawang Merah Hidayatullah, Bryan Adam; Nugroho, Bangkit Indarmawan; Santoso, Nugroho Adhi; Gunawan, Gunawan
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.10506

Abstract

Penelitian ini membandingkan metode fuzzy Mamdani dan fuzzy Tsukamoto dalam mengidentifikasi tingkat serangan penyakit pada tanaman bawang merah untuk meningkatkan deteksi dini penyakit dan produktivitas pertanian. Menggunakan dataset parameter kesehatan tanaman, termasuk gejala penyakit dan kondisi lingkungan, penelitian mengaplikasikan kedua metode fuzzy tersebut untuk memperkirakan kerentanan tanaman terhadap penyakit. Hasil menunjukkan bahwa fuzzy Tsukamoto lebih akurat dan efisien, terutama dalam data kompleks. Penelitian ini memberikan pemahaman baru dalam aplikasi fuzzy logic pada penyakit tanaman bawang merah dan pengembangan model serupa di pertanian. Temuan ini penting untuk pengembangan sistem pendukung keputusan yang lebih efisien dalam pertanian, mengintegrasikan teknologi informasi dalam manajemen kesehatan tanaman.
Penerapan Metode Fuzzy K-Means Clustering untuk Pengelompokan Konten Halaman Web secara Otomatis Budiono, Wahyu; Nugroho, Bangkit Indarmawan; Santoso, Nugroho Adhi; Gunawan, Gunawan
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research (Special Issue)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.12022

Abstract

Penerapan Metode Fuzzy K-Means Clustering untuk Pengelompokan Konten Halaman Web Secara Otomatis adalah penelitian yang bertujuan untuk mengotomatisasi proses pengelompokan konten halaman web menggunakan pendekatan clustering fuzzy. Dalam konteks ini, algoritma Fuzzy K-Means digunakan untuk mengelompokkan konten halaman web menjadi beberapa kategori berdasarkan kesamaan karakteristik tertentu. Metode ini memanfaatkan kelebihan pendekatan clustering fuzzy dalam menangani ketidakpastian dalam data dan kemampuan K-Means dalam mengelompokkan data menjadi beberapa cluster. Penelitian ini mencakup tahapan pra-pemrosesan data, ekstraksi fitur, dan implementasi algoritma Fuzzy K-Means Clustering. Eksperimen dilakukan menggunakan dataset yang berisi konten halaman web dari berbagai domain. Hasil evaluasi menunjukkan bahwa metode ini dapat menghasilkan pengelompokan konten halaman web yang sesuai dengan karakteristiknya secara otomatis, dengan tingkat akurasi dan interpretabilitas yang baik. Implementasi metode ini dapat memberikan kontribusi signifikan dalam pengelolaan dan penyaringan konten web secara efisien.
Penerapan Metode Fuzzy K-Means Clustering untuk Pengelompokan Konten Halaman Web secara Otomatis Budiono, Wahyu; Nugroho, Bangkit Indarmawan; Santoso, Nugroho Adhi; Gunawan, Gunawan
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research (Special Issue)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.12065

Abstract

Penerapan Metode Fuzzy K-Means Clustering untuk Pengelompokan Konten Halaman Web Secara Otomatis adalah penelitian yang bertujuan untuk mengotomatisasi proses pengelompokan konten halaman web menggunakan pendekatan clustering fuzzy. Dalam konteks ini, algoritma Fuzzy K-Means digunakan untuk mengelompokkan konten halaman web menjadi beberapa kategori berdasarkan kesamaan karakteristik tertentu. Metode ini memanfaatkan kelebihan pendekatan clustering fuzzy dalam menangani ketidakpastian dalam data dan kemampuan K-Means dalam mengelompokkan data menjadi beberapa cluster. Penelitian ini mencakup tahapan pra-pemrosesan data, ekstraksi fitur, dan implementasi algoritma Fuzzy K-Means Clustering. Eksperimen dilakukan menggunakan dataset yang berisi konten halaman web dari berbagai domain. Hasil evaluasi menunjukkan bahwa metode ini dapat menghasilkan pengelompokan konten halaman web yang sesuai dengan karakteristiknya secara otomatis, dengan tingkat akurasi dan interpretabilitas yang baik. Implementasi metode ini dapat memberikan kontribusi signifikan dalam pengelolaan dan penyaringan konten web secara efisien.
SYTEMATIC LITERAURE REVIEW : PENERAPAN METODE ALGORITMA C4,5 UNTUK KLASIFIKASI Srifani, Dewi; Nugroho, Bangkit Indarmawan; Santoso, Nugroho Adhi
Jurnal Informatika UPGRIS Vol 8, No 2: Desember 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i2.12507

Abstract

Abstract---Data mining is extracting data in processing information with the aim of finding important patterns in piles of data. With data mining we can classify, predict, and make a decision. Classification is a way of grouping a data according to the characteristics of a data to be classified. In the process, the classification is divided into two, namely manually and with the help of technology. Manual classification is a classification carried out by humans without the help of technology, while classification with the help of technology has several algorithms, including C4.5, Naive Bayes, Fuzzy, and K-Nearest Neighbor. 5 for classification, a systematic approach is used in the form of a systematic literature review (SLR). SLR is defined as a process in which the identification, assessment, and interpretation of all available research evidence is carried out with the aim of answering a number of research questions
Comparison of naïve bayes and KNN for herbal leaf classification Nugroho, Bangkit Indarmawan; Khusni, Muhammad Wazid; Ananda, Pingky Septiana; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.297

Abstract

This study aims to compare the effectiveness of two classification algorithms, namely Naïve Bayes Classifier and K-Nearest Neighbor (KNN), in classifying herbal leaves. This research design uses a quantitative approach with experimental analysis and model validation. The dataset consisted of images of papaya leaves, pandanus, cat's whiskers, and betel nut taken in different lighting conditions. The methodology includes pre-processing of data by converting images into grayscale, feature extraction using Gray Level Co-occurrence Matrix (GLCM), and application of Naïve Bayes and KNN algorithms. The main results showed that KNN achieved 90.00% accuracy with precision, recall, and F1-score of 88.33% respectively, higher than Naïve Bayes which had 82.50% accuracy, 81.46% precision, 85.83% recall, and 82.27% F1-score. In conclusion, KNN is superior in the classification of herbal leaves to Naïve Bayes, although it requires a longer computational time. Further research is recommended to optimize algorithm parameters and explore the integration of deep learning techniques to improve classification accuracy and efficiency.
Application of centroid and geometric mean methods for face recognition Nugroho, Bangkit Indarmawan; Khasanah, Apriliani Maulidya; Arif, Zaenul; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.300

Abstract

Face recognition is one of the most important areas in artificial intelligence and image processing, with wide applications from attendance system security to human-computer interaction. This study aims to overcome the difficulties in classifying student faces in an academic environment by applying and comparing centroid and geometric mean methods. Student face data was collected and processed through conversion to grayscale, pixel intensity normalization, and statistical analysis using both methods. The results showed that both methods had the same performance with 70% accuracy, 75% precision, 60% recall, and 66.67% F1-score. The application of this method can improve the efficiency and accuracy of attendance management and security in the campus environment, especially for institutions with limited resources.
Application of WASPAS method in determining the best flour for nastar making Nugroho, Bangkit Indarmawan; Dewi, Errika Mutiara; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.303

Abstract

This study explores the use of the Weighted Aggregated Sum Product Assessment (WASPAS) Method in selecting the best wheat flour for pineapple cake production. The aim of this study is to develop a more systematic and quantitative approach in assessing flour quality, provide useful guidance for pineapple cake producers and enrich the academic literature in the field of food science and food technology. This study used quantitative methodology data analysis and model validation with WASPAS, aimed at overcoming the challenge of selecting the best wheat flour for pineapple cake making. Results showed that the WASPAS method was effective in identifying the best flour, with Bungasari Hana Emas flour obtaining the highest WASPAS score of 0.952863, followed by the Falcon Hijau with a score of 0.931373. This score indicates the optimal balance between cost and quality. The study emphasizes the importance of objective decision-making tools in the food industry, suggesting that such an approach can significantly improve product quality and production efficiency.
Applying certainty factor method to identify diseases in rice plants Nugroho, Bangkit Indarmawan; Miftakhuddin, Ahmad; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.310

Abstract

Rice (Oryza Sativa L) is the most important food crop in the world after wheat and corn, as well as the main source of protein for most of the world's population, especially in Asia. The Save Swamps for Prosperous Farmers (Serasi) program in Central Java Territory cannot run well considering the tall capacity of existing rice agriculturists to bargain with bugs and maladies of the rice they plant, so it is essential to make a device within the frame of an master framework for diagnosing rice plant infections.  For this reason, it is very important to be aware of the factors that influence production levels. Disease is one of the most detrimental factors in rice production, where many losses are caused by disease. Each of these diseases generally shows symptoms of the disease suffered before it reaches a more severe and widespread stage, these symptoms can be recognized by carrying out a diagnosis first. This can be done using an expert system. In this research, an expert system was utilized which was made utilizing the certainty figure strategy, with a test of 25 ranchers within the West Tegal Area, Tegal City. From the comes about of the inquire about carried out, it was concluded that with this framework the level of exactness obtained using the posttest contains a esteem of 100%, in other words the framework encompasses a decently tall level of accuracy.
Co-Authors -, Muh Musthafa Yoshananda Aang Alim Murtopo Adhi Santoso, Nugroho Ananda, Pingky Septiana Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Arif, Zaenul Asyfiya, Dzurrotu Umi Aziz, Taufiq Azmi, Muchamad Nauval Bayu Aji Santoso Budiono, Wahyu Defi Lugianti Dewi, Errika Mutiara Erni Unggul Sedya Utami, Erni Unggul Sedya Fajrul Falah Farkhan, Muhammad Fatkhurrohman Fatkhurrohman, Fatkhurrohman Firmansyah, Akhmad Lutfi Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Handayani, Sri Hidayatullah, Bryan Adam Isnaeni Hamidah Karsidin, Karsidin Khasanah, Apriliani Maulidya Khusni, Muhammad Wazid Kurniawan, Rifki Dwi Maulana, M Taufik Fajar Miftakhuddin, Ahmad Mohamad Rifki Septiadi Mohammad Amin Triwinanto Muhammad Syahrul Maulana Muhammad Syahrul Maulana Mustofa, Fahmi Charish Naja, Naella Nabila Putri Wahyuning Nindy Putri Lestari Nugroho Adhi Santoso Nugroho Adhi Santoso Nursahid, Wahyu Nurul fadillah, Nurul Rafhina, Ana Rifki Dwi Kurniawan Rifki Dwi Kurniawan Romadhona, Wahyu Rozak, Fatkhur Santoso, Bayu Aji Santoso, Nugroho Adh Santoso, Nugroho Adhi Sarif Surorejo Sedya Utami, Erni Unggul Sedya Sekar Setyaningtyas Septiana, Pingky Srifani, Dewi Supratman, Ardhi Syefudin Syefudin Syefudin Syefudin, Syefudin Umar Ali Ahmad Wahyu Romadhona Wresti Andriani Yoshananda, Muh Musthafa Zaenal Arif Zaenal Ma'arif Zaenul Arif Zaenul Arif Zaenul, Arif