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All Journal Techno.Com: Jurnal Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika dan Rekayasa Perangkat Lunak BUDGETING : Journal of Business, Management and Accounting International Journal of Artificial Intelligence and Robotics (IJAIR) Journal of Soft Computing Exploration Jurnal Citrakara Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Seminar Nasional Teknologi dan Multidisiplin Ilmu
Ery Mintorini
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation Other

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Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Implementasi Profile Matching Pada Sistem Pendukung Keputusan Seleksi Peserta Tenda Kewirausahaan Setiawan, Aries; Nuryanto, Imam; Mintorini, Ery; Hidajat, Moch Sjamsul; Farida, Ida; Widjajanto, Budi; Prasetya, Jaka; Lewa, Andi Hallang; Karmila, Karmila
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11176

Abstract

Salah satu program dari Unit Kewirausahaan adalah program peserta tenda wirausaha.. Pada proses penilaian manual, pemilihan peserta hanya berdasarkan jenis produk wirausaha yang akan ditawarkan. Namun hal tersebut tidak mendapatkan hasil seleksi yang maksimal karena jika seleksi yang ada hanya menggunakan satu komponen variabel dan penilaian tersebut cenderung mengandung unsur yang tidak  berpotensi. Salah satu metode pengambilan keputusan yang mempunyai bobot dalam perhitungannya adalah pencocokan profil. Pencocokan profil bekerja dengan memberikan nilai standar pada setiap variabel dan nilai tertimbang juga diberikan pada variabel tersebut. Selanjutnya dicari perbedaan nilai nilai partisipan dan nilai standar masing-masing variabel. Hasil pemeringkatan yang dihasilkan dari pencocokan profil merupakan gabungan dari beberapa variabel dengan tingkat bobot yang berbeda-beda. Oleh karena itu, dalam penilaian pemilihan peserta tenda wirausaha sebaiknya menggunakan pola perhitungan yang dimiliki dengan metode profile matching. Bobot masing-masing variabel ditentukan oleh pengambil keputusan dalam hal ini kepala Kewirausahaan. Dengan persentase nilai bobot yang berbeda-beda pada setiap variabel akan memberikan hasil penilaian yang sesuai dengan tingkat kompetensi peserta seleksi tenda wirausaha
Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification Hidajat, Moch. Sjamsul; Wibowo, Dibyo Adi; Mintorini, Ery
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2059

Abstract

Ikat weaving is a rich traditional heritage of Kota Kediri, Indonesia, with a diverse array of intricate motifs that reflect the cultural richness of the region. As new motifs emerge and information about older designs fades, manual identification becomes time-consuming and difficult. This study leverages machine learning technology, specifically XGBoost, Random Forest, and Neural Network algorithms, to automate the classification of these weaving patterns. The dataset consisted of 600 images, split into 480 images (80%) for training and 120 images (20%) for testing, representing four distinct weaving motifs: "Gumul Weaving, Bolleches Weaving, Kuda Kepang Weaving, and Sekar Jagad Weaving." The study achieves high accuracy, with precision, recall, and F1-score all reaching 100%, underscoring its potential to not only improve the efficiency of motif identification, but also play a crucial role in preserving and promoting Indonesia's cultural heritage. Future research should focus on further optimizing these algorithms and expanding datasets to capture a broader range of ikat motifs. Additionally, enhancing the application of this model can contribute to a deeper understanding and broader appreciation of Kota Kediri’s cultural wealth through digital platforms.
Implementasi Profile Matching pada Sistem Pendukung Keputusan Seleksi Peserta Tenda Kewirausahaan Setiawan, Aries; Nuryanto, Imam; Mintorini, Ery; Hidajat, Moch. Sjamsul; Farida, Ida; Widjajanto, Budi; Prasetya, Jaka; Lewa, Andi Hallang; Karmila, Karmila
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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

Abstract

One of the programs from the Entrepreneurship Unit is the entrepreneurial tent participant program. In the manual assessment process, participant selection is only based on the type of entrepreneurial product that will be offered. However, this does not get maximum selection results because if the existing selection only uses one variable component and the assessment tends to contain elements that have no potential. One decision making method that has weight in its calculations is profile matching. Profile matching works by assigning a standard value to each variable and a weighted value is also assigned to the variable. Next, look for differences in participant scores and standard scores for each variable. The ranking results resulting from profile matching are a combination of several variables with different weight levels. Therefore, in assessing the selection of entrepreneurial tent participants, it is best to use the existing calculation pattern using the profile matching method. The weight of each variable is determined by the decision maker, in this case the head of Entrepreneurship. With different percentage weight values ​​for each variable, it will provide assessment results that are in accordance with the level of competency of the entrepreneurial tent selection participants.
Penerapan Kombinasi Forward Chaining Dan Naive Bayes Untuk Mendeteksi Penyakit Pada Burung Merpati Balap Mintorini, Ery; Mahmud, Wildan; Zahari, Iqlima; Moch. Sjamsul; Widyatmoko, Widyatmoko; Wibowo, Toni; Ferdianto , Bhekti Eka
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 7 No. 2 (2023): PROSIDING SEMINAR NASIONAL INOVASI TEKNOLOGI TAHUN 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v7i2.3513

Abstract

Penelitian ini bertujuan untuk membangun sebuah sistem yang dapat digunakan dalam mendiagnosa penyakit pada burung merpati balap. Sistem ini menggunakan kombinasi antara metode forward chaining dengan naive bayes. Forward chaining digunakan untuk menelusuri gejala-gejala yang dialami kemudian membuat konklusi penyakit sedangkan algoritma naive bayes digunakan untuk mencari nilai probabilitas tertinggi dari kemungkinan penyakit yang dialami berdasarkan pada gejala yang dirasakan. Data dalam penelitian ini berupa data gejala, data penyakit dan data aturan. Hasil percobaan yang dilakukan menyimpulkan bahwa kombinasi forward chaining dan naive bayes mampu menghasilkan keputusan yang akurat dan presisi untuk mendiagnosa penyakit pada burung merpati balap.
Co-Authors Ainun Muhtadin Ali, Rabei Raad Angel Frilyaningrum Arika Norma Wahyu Dorroty Bonifacius Vicky Indriyono Budi Widjajanto Chandriana, Tri Dian Christy Atika Sari Dibyo Adi Wibowo Didik Hermanto Dimentieva, Imelda Eko Hari Rachmawanto Erika Devi Udayanti Erna Daniati Ferdianto , Bhekti Eka Gilang Rizky Gardianto Hyperastuty, Agoes Santika Ichwan Setiarso Ida Farida Imam Nuryanto Indriyono, Bonifacius Jaka Prasetya karmila karmila Lewa, Andi Hallang Mellati, Pita Moch. Sjamsul Moch. Sjamsul Hidajat Natalinda Pamungkas Pamungkas, Natalinda Pratama, Zudha Prayugo, Moh. Lambang Putri Hartarani, Wiedya Sanina Quamila Putri Setiawan, Aries Setiawan, Fachruddin Ari Tri Esti Rahayuningtyas Wibowo, Toni Widyatmoko Widyatmoko Wildan Mahmud Wisnu Pratama Zahari, Iqlima