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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 EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Teknosains : Jurnal Sains,Teknologi dan Informatika International Journal of Artificial Intelligence and Robotics (IJAIR) Mathematics and Applications (MAp) Journal Jurnal Ekonomi dan Manajemen (JEKMA) Journal of Artificial Intelligence and Digital Business Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Kepada Masyarakat Jurnal Maisyatuna Synergy In Economic & Business Management (SEBM)
Moch. Sjamsul Hidajat
Universitas Dian Nuswantoro Kediri
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Covid-19 Classification using Convolutional Neural Networks Based on Adam, RMSP, and SGD Optimalization Hidajat, Moch Sjamsul; Wibowo, Dibyo Adi
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9492

Abstract

In this comprehensive study, a meticulous analysis of the application of Convolutional Neural Network (CNN) methodologies in the classification of Covid-19 and non-Covid-19 cases was conducted. Leveraging diverse optimization techniques such as RMS, SGD, and Adam, the research systematically evaluated the performance of the CNN model in accurately discerning intricate patterns and distinct features associated with Covid-19 pathology. the implementation of the RMS and Adam optimization methods resulted in the highest accuracy levels, with both models achieving an impressive 98% accuracy in the classification of Covid-19 and non-Covid-19 cases. Leveraging the robust capabilities of these optimization techniques, the study successfully demonstrated the effectiveness of the RMS and Adam models in enhancing the precision and reliability of the Convolutional Neural Network (CNN) for the accurate identification and differentiation of Covid-19 patterns within the medical imaging datasets. The notable achievement of 98% accuracy further emphasizes the potential of these optimization methods in advancing the capabilities of CNN-based diagnostic tools, thus contributing significantly to the ongoing efforts in Covid-19 diagnosis and management.  
Predicting Gold Price Movement Using Long Short-Term Memory Model Nagata, Azaria Beryl; Hidajat, Moch Sjamsul; Wibowo, Dibyo Adi; Widyatmoko, Widyatmoko; Yaacob, Noorayisahbe Bt Mohd
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10305

Abstract

Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.
Data Mining Application Analyzing Customer Purchase Patterns Using The Apriori Algorithm Prayugo, Moh. Lambang; Wibowo, Dibyo Adi; Hidajat, Moch. Sjamsul; Mintorini, Ery; Ali, Rabei Raad
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10308

Abstract

The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.
Combination Concept of LSB and PlayFair Cipher for Optimizing Data Security Hidajat, Moch. Sjamsul; Ery Mintorini
Jurnal Informatika Polinema Vol. 10 No. 3 (2024): Vol. 10 No. 3 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i3.5132

Abstract

Data security is now something that is very necessary, with the aim of ensuring that important data and information does not fall into the hands of unauthorized people. The widespread data exchange process provides opportunities for unauthorized parties to take, copy or steal the exchanged data. This is what triggers the importance of securing data when exchange occurs. In the field of computer science, there are many ways that data can be secured. These methods include the concepts of steganography and cryptography. Steganography is a way to hide data in various media, while cryptography is a way to encode data into a form that has no meaning. This research aims to design a system to secure messages in the form of text data using the Playfair cipher cryptography method and Least Significant Bit (LSB) steganography using image media and the image containing the message is not visible to the eye that the image contains a secret message
Dampak Non Performing Loan Terhadap Peningkatan Pendapatan Perusahaan di Sektor Jasa Perbankan Widyatmoko Widyatmoko; Wildan Mahmud; Moch. Sjamsul Hidajat; Stevani Tri Wahyu Putri
Maisyatuna Vol 4 No 4 (2023): Oktober : Jurnal Maisyatuna
Publisher : STAI Denpasar Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53958/mt.v4i4.386

Abstract

It is hoped that the results of this research can be used as input and consideration for companies in making decisions and establishing policies to overcome problems related to the level of Non-Performing Loans of banks, especially Rural Banks. The population of this research are customers who use credit services who have experienced problems, especially in the non-performing loan category. The research uses quantitative research methods with analysis using the SPSS application and using multiple regression analysis techniques. In this research, the results can prove that the results of Non-Performing Loans will influence the increase in income. The results of the t-test analysis = 3.578 means that the hypothesis can be accepted, because the t-test > tcount. namely 3.578 > 3.078, meaning there is a correlation between the Non-Performing Loan variable and an increase in company income. Keywords: non-performing loans, increasing income, banking
Klasifikasi Stunting pada Balita menggunakan Algortima Gradient Bossting Clasifier Azhari, Daffa Maulana; Hidajat, Moch Sjamsul
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27502

Abstract

Stunting is a significant public health problem, impacting the physical and cognitive growth and development of children under five. In Indonesia, stunting is a major issue caused by a lack of nutritional intake since birth, including in the city of Semarang. This study aims to compare the performance of K-Nearest Neighbor (KNN), Naïve Bayes, and Gradient Boosting Classifier algorithms in classifying stunting in toddlers, to find the best model. The data used is quantitative data from posyandu, consisting of 1288 samples with variables including Name, Gender, Age, Date of Birth, Parent's Name, Village, Rt, RW, weight, height, arm circumference, and Z-score. After data collection, a data preprocessing process is carried out to clean and prepare the data. The data was divided into training and test data with a ratio of 80:20, 70:30, and 60:40, which were then trained and tested using the three algorithms. The best model was further evaluated with K-Fold Cross Validation to assess the stability and generalizability of the predictions. Model evaluation uses accuracy, precision, recall and F1-Score metrics. The results showed that Gradient Boosting Classifier gave the best performance with 99.92% accuracy, 99.92% precision, 99.92% recall, and 99.92% F1-score. This study concludes that the Gradient Boosting Classifier is the most optimal model in the classification of stunting in toddlers, giving the best precision results.
PREFERENSI KONSUMEN TERHADAP PILIHAN MAKANAN BERBASIS DIGITAL MELALUI APLIKASI PEMESANAN ONLINE DALAM PERILAKU KONSUMEN Widyatmoko; Tri Esti Rahayuningtyas; Wildan Mahmud; Moch. Sjamsul Hidajat
Jurnal Ekonomi dan Manajemen Vol. 3 No. 3 (2024): Oktober : Jurnal Ekonomi dan Manajemen
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jekma.v3i3.1692

Abstract

Penelitian ini bertujuan untuk mengeksplorasi bagaimana kemudahan penggunaan dan fitur aplikasi pemesanan makanan online mempengaruhi perilaku serta kepuasan konsumen, serta bagaimana kualitas informasi yang disediakan oleh aplikasi mempengaruhi keputusan pembelian. Masalah utama yang dihadapi adalah oversaturation pasar aplikasi pemesanan makanan yang membingungkan konsumen dan kualitas informasi yang tidak konsisten, seperti akurasi ulasan dan relevansi promosi, yang dapat mengurangi kepuasan. Metode penelitian yang digunakan adalah kualitatif dengan wawancara mendalam, diskusi kelompok fokus (FGD), dan analisis konten ulasan pengguna untuk mendapatkan wawasan mendalam mengenai preferensi konsumen. Hasil penelitian menunjukkan bahwa kemudahan penggunaan dan fitur aplikasi yang intuitif secara signifikan mempengaruhi kepuasan pengguna, dengan fitur pelacakan pesanan dan promosi sebagai faktor penting dalam keputusan pembelian. Selain itu, kualitas informasi seperti akurasi ulasan dan relevansi promosi berperan besar dalam membangun kepercayaan dan mempengaruhi keputusan pembelian. Penelitian ini menyimpulkan bahwa aplikasi yang mudah digunakan, menyediakan informasi berkualitas tinggi, dan menawarkan promosi yang relevan dapat meningkatkan pengalaman pengguna dan efektivitas strategi pemasaran. Rekomendasi diberikan untuk merancang aplikasi yang lebih user-friendly dan memastikan kualitas informasi yang akurat.
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.
Co-Authors Ainun Muhtadin Ali, Rabei Raad Angel Frilyaningrum Aulia Rahmawati Azhari, Daffa Maulana Bhekti Eka Ferdianto Bonifacius Vicky Indriyono Budi Widjajanto Choerul Umam Daffa Hafizh Dzamara Daffa Maulana Dea Debora Aprillia Dibyo Adi Wibowo Ery Mintorini Ery Mintorini Evita Citra Yustiqomah Ichwan Setiarso Ida Farida Iffah Irdinawati Imam Nuryanto Iqlima Zahari Jaka Prasetya karmila karmila Lewa, Andi Hallang Muhammad Burhanudin, Muhammad Nagata, Azaria Beryl Natanael, David Febrian Pratama, Zudha Prayugo, Moh. Lambang Puspa, Silfi Andriana Rizal Muhammad Affandi Setiawan, Aries Stevani Tri Wahyu Putri Toni Wibowo Tri Esti Rahayuningtyas Widyatmoko Widyatmoko Wildan Mahmud Yaacob, Noorayisahbe Bt Mohd Yulianti, Shinta Rachma