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All Journal Techno.Com: Jurnal Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Jurnal Penelitian J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Tekno Kompak INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Jurnal Mantik Journal Zetroem Archive: Jurnal Pengabdian Kepada Masyarakat Jikom: Jurnal Informatika dan Komputer Jurnal Pengabdian Harapan Bangsa Software Development Digital Business Intelligence and Computer Engineering Jurnal Informatika Polinema (JIP) Jurnal ELTEK Cahaya Pengabdian
Sepyan Purnama Kristanto
Politeknik Negeri Banyuwangi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Rancang Bangun Sistem Presensi Menggunakan Deteksi Wajah dan Suhu Tubuh Berbasis Raspberry Pi Widodo, Agung Wahyu; Ratnasari, Avina Dwi; Hakim, Lutfi; Kristanto, Sepyan Purnama
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 2 No. 1 (2023): SESSION (SEPTEMBER)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v2i1.2023.26-33

Abstract

Upaya preventif dalam meminimalisir penyebaran virus Covid-19 terus dilakukan. Salah satunya adalah dengan melakukan presensi dengan menggunakan metode tanpa kontak dan pencatatan suhu pengguna secara langsung. Anjuran untuk tidak menyentuh atau tidak melakukan fisik langsung ini mempengaruhi perubahan metode presensi karyawan atau tenaga kerja dalam melakukan cek kehadiran di tempat kerja. Hal ini juga menuntut setiap instansi/perusahaan untuk dapat menyesuaikan dengan perkembangan teknologi agar kinerja dari instansi tersebut efektif. Pada penelitian ini dilakukan pengembangan sistem presensi dengan menggunakan metode pengenalan wajah sebagai autentikasi pengguna dan deteksi suhu untuk memonitor kondisi pegawai yang melakukan presensi. Sistem dikembangkan dengan menggunakan model pengenalan wajah yang telah dikembangkan pada penelitian sebelumnya dan menggunakan sensor Thermal untuk mendeteksi suhu tubuh pegawai. Metode pengembangan sistem menggunakan metode RAD yang telah disesuaikan dengan kebutuhan sistem. Hasil yang didapatkan menunjukkan bahwa sistem presensi yang digunakan dapat berjalan sesuai dengan yang diharapkan dimana didapatkan seluruh fitur yang dikembangkan 100% sesuai dengan kebutuhan yang telah didefinisikan dan dapat berjalan 100% sesuai dengan scenario pengujian yang dilakukan. Selain itu, pada pengujian pada perangkat sensor, sensor kamera dapat menerima foto pengguna sampai intensitas jarak 15 m, sedangkan sensor suhu dapat terbaca dengan benar sampai dengan jarak 2 meter.
Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture Hakim, Lutfi; Kristanto, Sepyan Purnama; Yusuf, Dianni; Shodiq, Mohammad Nur; Setiawan, Wahyu Ade
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 5 No 2 (2021): August 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (843.916 KB) | DOI: 10.29407/intensif.v5i2.15287

Abstract

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.
Classification of Dragon Fruit Stem Diseases Using Convolutional Neural Network Hakim, Lutfi; Asyhari, Aditya Roman; Kristanto, Sepyan Purnama; Yusuf, Dianni; Prasetyo, Junaedi Adi; Siregar, Hamdan Maruli
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.20093

Abstract

A holticulture plant known as dragon fruit (pitaya) is a fruit that has many benefits and is widely cultivated by farmers in several areas of Banyuwangi. In dragon fruit plants there are various kinds of diseases that attack including red spot, stem rot, black rot, scab, and mosaic. Farmers still recognize diseases on dragon fruit stems manually so that sometimes there are errors in disease recognition. In this research, a system was developed to identify the types of diseases on dragon fruit stems. This system was built by proposing the Convolutional neural network method with the proposed architecture using the Python programming language with the Tensorflow, Keras, and Scikit-Learn libraries. The proposed system is tested using k-fold cross validation with tunning parameters fold = 5 and epoch = 5. The training results show that the highest accuracy performance value is 85.06% with the data used as test data as many as 191 images producing 147 correct data and 44 data wrong, while the average overall accuracy score was 76.43%.
Ekstraksi Informasi Destinasi Wisata Populer Jawa Timur Menggunakan Depth-First Crawling Sepyan Purnama Kristanto; Lutfi Hakim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1081

Abstract

Travel Destinations are an inseparable part of human life today. As one of the provinces with a large area, East Java is one of the most visited areas for its tourism. Many people are competing in finding information related to these tourist destinations on the internet, one of which is the Tripadvisor application. Of the many tourist attractions, several tourist attractions have different attractions and experiences each time. Tourists have widely used the Tripadvisor application in determining the location where they will visit on their vacation activities. With various features ranging from reviews and recommendations for sharing photos, TripAdvisor is one of the best applications in the inventory of tourist attractions. Of the many tourist destinations, it is necessary to analyze and evaluate both tourist attractions that have many visitors with tourist attractions that are rarely visited by both local and foreign visitors. This goal, information mining (web mining), was carried out on the TripAdvisor application to obtain information on East Java Province's popular destinations. Crawling results on the TripAdvisor website, obtained various kinds of information such as names of tourist attractions, locations, visitor reviews, photos, and ratings of these tourist attractions. Spatial Analysis, a Tourist Sentiment Analyst on tourist objects, can then be carried out. It can also be developed into the recommendation system for the best tourist attractions in East Java Province
Klasifikasi Koleksi Perpustakaan Berbasis Dewey Decimal Classification Menggunakan Algoritma TF-RF dan K-Nearest Neighbor Anis Sulala; Lutfi Hakim; Sepyan Purnama Kristanto; Ruth Ema Febrita; Khoirul Umam
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9663

Abstract

Perpustakaan Politeknik Negeri Banyuwangi memiliki banyak koleksi buku dari berbagai bidang keilmuan yang terus bertambah setiap tahunnya. Pertambahan koleksi ini menimbulkan tantangan dalam proses pengklasifikasian buku yang masih dilakukan secara manual, sehingga memakan waktu dan tenaga yang besar. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi koleksi perpustakaan berbasis Dewey Decimal Classification (DDC) menggunakan algoritma Term Frequency-Relevance Frequency (TF-RF) dan K-Nearest Neighbor (kNN). Data yang digunakan berupa judul buku berbahasa Indonesia dengan jumlah 4056 data pada 73 label. Prosesnya dimulai dari pre-processing, ekstraksi fitur menggunakan TF-RF untuk mengukur relevansi kata kunci dan pengklasifikasian model dengan kNN, serta evaluasi performa model dengan K-Fold Cross validation 10. Hasil penelitian ini menunjukkan bahwa sistem klasifikasi yang dikembangkan mampu menghasilkan akurasi terbaik sebesar 74,38% dan presisi 78,91% dengan tunning parameter menggunakan pendekatan GridSearch pada parameter K=1 yang diperoleh pada skenario ketiga dengan 7 kelas dan 2831 data. Penelitian ini membuktikan bahwa kombinasi TF-RF dan KNN efektif diterapkan untuk klasifikasi teks koleksi buku.
Penguatan Layanan Informasi dan Laporan Masyarakat di Desa Kalirejo melalui Implementasi Chatbot WhatsApp Terintegrasi Sistem Dashboard Lutfi Hakim; Sepyan Purnama Kristanto; Galih Hendra Wibowo
Archive: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2026): Juni 2026
Publisher : Asosiasi Pengelola Publikasi Ilmiah Perguruan Tinggi PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55506/arch.v5i2.272

Abstract

Latar Belakang: Desa Kalirejo menghadapi permasalahan dalam pelayanan publik, khususnya terkait dengan sistem informasi dan laporan keluhan masyarakat yang masih bersifat konvensional, sehingga menyebabkan layanan informasi belum bisa optimal sampai ke masyarakat dan data laporan tidak terintegrasi dan sulit dimonitor. Tujuan: Tujuan dari program ini adalah melakukan penguatan layanan informasi dan laporan masyarakat melalui implementasi sistem perpesanan WhatsApp berbasis Chatbot yang terintegrasi dengan sistem Dashboard berbasis web. Metode: Pendekatan Participatory Action Research digunakan pada program ini yang menempatkan mitra sebagai subjek aktif dalam setiap tahap kegiatan yang meliputi 5 tahapan kegiatan mulai dari tahapan identifikasi dan pemetaan masalah sampai dengan evaluasi dan monitoring. Hasil: SI KARJO dapat diimplementasikan dengan baik dan mitra mampu mengoperasikannya secara mandiri melalui pendampingan berkelanjutan. Kesimpulan: Program ini mendapatkan apresiasi positif dengan skor survey kepuasan mitra sebesar 95,82%.
Prediksi Harga Ikan Koi Berbasis Analisis Morfometrik Menggunakan Algoritma Random Forest Regressor Sepyan Purnama Kristanto; Lutfi Hakim; Dianni Yusuf; Moh. Erdda Habiby
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8617

Abstract

The lack of clarity in koi fish (Cyprinus rubrofuscus) pricing within the Indonesian ornamental fish market, driven by subjective valuation practices and information asymmetry, remains a primary challenge creating significant price disparities. The primary objective of this research is to address this challenge by designing and evaluating an objective predictive model. As its main contribution, this study develops the first Random Forest Regressor (RFR) based price prediction model. This model is specifically designed to handle complex non-linear relationships by integrating three main feature groups: morphometric parameters (species, size) and phenotypic characteristics (color patterns). Using a dataset of 800 samples collected from koi breeding centers in East Java, the optimized model achieved solid predictive performance, indicated by a Coefficient of Determination (R²) of 0.85 and a Root Mean Squared Error (RMSE) of IDR 265,000. Feature importance analysis revealed the significant finding that fish variety (one of the three analyzed feature groups) is the most dominant price determinant (62% contribution). The model quantitatively validates that rare varieties (such as Tancho/Utsuri) are valued 3 to 5 times higher than common varieties of the same size. Comparative analysis with traditional linear regression models (R² 0.61) also demonstrated the RFR's superiority in capturing complex morphological feature interactions. A critical finding indicates that the model's accuracy, already strong in the non-premium segment, can be improved by up to 15% through the quantification of qualitative aesthetic attributes (such as kiwa or gradation) using computer vision. The implementation of this model has the potential to standardize koi valuation, reduce market information asymmetry by up to 40%, and serve as a foundation for the development of the first AI-based price recommendation system in Indonesia's aquaculture industry.
Co-Authors Adhi Dharma Wibawa Aditya Roman Asyari Alfi Zuhriya Khoirunnisaa Anis Sulala Annisa Rani Maulida Aprilieno , Richo Malvin Arindo, Jastin Wiliam Arrizki, Mohammad Rashel Asyhari, Aditya Roman Ayatullah, Mohamad Dimyati Carenza Al Kharraz David Mahbubi Dian Taufik Dianni Yusuf Dianni Yusuf Dzikria, Jauhara Nada Egi Sabta Hiro Eka Amaliyah Ektrada, Erik Endi Sailul Haq Fahrus, Zhidan Faris Bagas Dinan Farisqi Panduardi Farizqi Panduardi Ferdiansyah, Erik Firmansyah, Ferdian Fitri Nur Afia Francisca Hariyati C Galih Hendra Wibowo Hadi Rizaldi Rahmanto Hendra Wibowo, Galih I Putu Sudhyana Mecha Imami, Marta Fairus Hani Indira Nuansa Ratri Junaedi Adi Prasetyo Khoirul Umam Kusuma, Dedy Hidayat Liliyani Amalia Lina Andriyani, Lina Lutfi Hakim Lutfi Hakim Maulia, Adinda Istika Moh. Erdda Habiby Mohammad Nur Shodiq Mohammad Nur Shodiq, Mohammad Nur Nanda Ayu Puspita Negara, Nanda Firdaus Pandu Nurul Hidayati Novitasari Pramana, Djien Purwoko, Agus Puspikawati, Septa Indra Puspita, Nanda Ayu Rahmad Adhi Sasono Ratnasari, Avina Dwi Rifqi, Mochamad Misbahur Rini, Eka Mistiko Ruth Ema Febrita Saputra, Mirza Dwiyan Setiawan, Wahyu Ade Siregar, Hamdan Maruli Suardinata, I Wayan Subono ., Subono Subono Subono Subono, Subono Suwardiyanto, Devit Tri Ayu Novitasari Utomo, Agus Priyo Wari, Naris Wibowo, Galih Hendra Widodo, Agung Wahyu Yogi Sukmawati Yuniarta Syarifatul Umami Yusup Supriyanto