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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Jurnal Informatika Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah EXPLORE Jurnal Mantik Jurnal Teknologi Informasi dan Multimedia International Journal of Informatics and Computation INFOKUM Infotek : Jurnal Informatika dan Teknologi Jurnal Kompilasi Hukum Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi EXPLORE Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology Explore Media Elektrik Green Engineering: Journal of Engineering and Applied Science Jurnal Informatika Jurnal Karya untuk Masyarakat (JKuM)
Imran, Bahtiar
Universitas Teknologi Mataram
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary

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CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN) Hambali, Hambali; Mahayadi, Mahayadi; Imran, Bahtiar
Jurnal Pilar Nusa Mandiri Vol 17 No 2 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i2.2705

Abstract

The diversity of tribes makes Indonesia rich in culture that characterizes it, one of which is traditional cloth. Through a variety of patterns and motifs that exist in traditional fabrics, reflecting the life, customs, and culture that exist in an area. Lombok is one of the areas that produces a typical songket cloth. The famous songket craft centers in Lombok are located in the Pringgasela area, Pringgasela District, Sade Village is in Pujut District, Central Lombok Regency and Sukarara is in Jonggat District, Central Lombok Regency. Each area of ​​the center for songket craftsmen has their own characteristics both in terms of the name, motif and texture. When viewed with the naked eye, the texture of each songket will look the same, to be able to know the differences in the texture of each songket, it is necessary to do a classification using computers or technology. Today's society still does not know much information about the textures of songket cloth. The method used to classify the typical Lombok songket in this study uses the Convolution Neural Network (CNN) method. The results obtained from the use of 64 datasets, with details of 40 types of Sade songket and 24 types of Pringgasela songket, after the dataset is trained it produces 86.36% accuracy, 87% precision, 86% recall, and 86% F1-Score. Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth.
DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER Imran, Bahtiar; Hambali, Hambali; Subki, Ahmad; Zaeniah, Zaeniah; Yani, Ahmad; Alfian, Muhammad Rijal
Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i1.2912

Abstract

This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost. The prediction results get Random Forest = 100%, Naïve Bayes = 80% and AdaBoost = 80%. Results using Test and Score with Number of Folds 2, 5 and 10. Number of Folds 2 Random Forest model Accuracy = 95%, Precision = 95% and Recall = 95%, Naïve Bayes Accuracy = 93%, Precision = 93% and Recall 93%, AdaBoost Accuracy = 90%, Precision = 90% and Recall = 90%. With Number of Folds 5 with Random Forest = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 93%, Precision = 93% and Recall = 93%. With Number of Folds 10 Random Forest model = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 92%, Precision = 92% and Recall = 92%. Of the 3 models used, Random Forest got the best classification results compared to the others.
LOMBOK PEARL QUALITY CLASSIFICATION USING A COMBINATION OF FEATURE EXTRACTION AND ARTIFICIAL NEURAL NETWORKS BASED ON SHAPE Imran, Bahtiar; Yani, Ahmad; Muslim, Rudi; Zaeniah, Zaeniah
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3507

Abstract

Lombok is attracted to the Moto GP event, which is held annually. Various tourism brands are owned by the island of Lombok, one of which is Mutiara. The ideal Pearl is perfectly round and smooth, but there are a variety of other shapes as well. One method that can be used to process Pearl's image is Computer Vision. For that, it is necessary to have a way to classify the quality of a Pearl based on its shape. The purpose of this study is to propose a system for pearl image classification by combining feature extraction with artificial neural networks. The method used in this study is GLCM feature extraction and Neural Networks. The proposed system can provide good classification results by combining the GLCM method and the Neural Network. This study uses Epochs 5, 10, 15, 30, 50, 100, 200, 300, and 500 with a learning rate of 0.5. The results of this study indicate that Epoch 100 gives the highest accuracy, 91.66%.
DECISION SUPPORT SYSTEM OF REWARDING ON LECTURER PERFORMANCE USING FUZZY TSUKAMOTO METHOD CASE STUDY AT MATARAM UNIVERSITY OF TECHNOLOGY Yani, Ahmad; Zenuddin, Z; Hambali, H; Muslim, Rudi; Imran, Bahtiar
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3548

Abstract

To prepare quality and character human resources, Mataram Technological University strives to provide the best in carrying out the tridharma activities of higher education, one of which is by giving rewards in the hope that morale and loyalty can continue to be improved. However, the gift-giving system that the Mataram Technological University has implemented has not been able to bring about change because the gift-giving system is incorrect. This is because the applied reward-giving assessment system only refers to the assessment without paying attention to other criteria in the tridharma of higher education. Such as the implementation of learning, Research, and community service. Therefore, to overcome this problem, a decision support information system for awarding lecturer performance is needed, which is built using the fuzzy Tsukamoto method by considering several criteria such as Presence, Research Results, and Community Service Results. Lecturer Performance Index in carrying out the learning process. With this decision support system, the implementation of the Tridharma carried out by lecturers can continue to monitor the system and improve the quality and accreditation of study programs and universities.
MAPPING LOCATIONS AND SHORTEST ROUTE OF TOURISM OBJECTS IN CENTRAL LOMBOK USING GIS-BASED A-STAR ALGORITHM Muslim, Rudi; Hidayatullah, Beni Ari; Imran, Bahtiar; Yani, Ahmad; Salman, Salman
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3927

Abstract

Central Lombok tourism is a tourism that foreign and domestic tourists often visit. There are many tourist objects offered by the Central Lombok Government, such as waterfall tours, beach tours, traditional village tours, cultural tours, and Pertamina Mandalika International Street Circuit. However, there are many tourist objects, and not all tourists know the location of these tourist objects. Tourists often experience constraints, are the location of tourist objects that is not quite right, it is still difficult to determine the shortest route to the location, and the lack of complete information about existing tourist objects, which can hinder the journey of tourists to the destination location. This study aims to map the location and shortest route of tourism objects in Central Lombok using an Android-based Geographic Information System by applying the A-Star algorithm. The results of this study are to develop an Android-based Geographic Information System or GIS by applying the star algorithm to Central Lombok tourism objects. So that the mapping of the location and information of tourist objects and obtain the search for the shortest route to tourist objects. The A-Star algorithm uses heuristic principles to find the shortest route to a tourism object and is optimal in finding the shortest route to tourism objects
Disease Detection of Rice and Chili Based on Image Classification Using Convolutional Neural Network Android-Based Muslim, Rudi; Zaeniah, Zaeniah; Akbar, Ardiyallah; Imran, Bahtiar; Zaenudin, Zaenudin
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4669

Abstract

The current development of machine learning makes it easier for humans to obtain information, especially from images. The presence of processing assistance from machines can increase the accuracy of the information provided to further convince the recipient of the information. Rice and chili farmers in Indonesia have experienced many disease attacks from several types of plant diseases. Not many farmers understand and are good at guessing the diseases that attack their rice and chili plants. So many rice and chili farmers experienced crop failure. This research aims to build a disease-detection system for rice and chili plants based on Android-based image classification. The machine learning method used is Convolutional Neural Network (CNN) with the Mobile Net version one model combined with the Sequential CNN and Tensor Flow Lite models. The results of the transfer learning evaluation on the Mobile Net version 1 model and the sequential CNN model obtained training accuracy of 0.88% with a loss of 0.34%, validation accuracy of 0.84% with a loss of 0.40%, and testing accuracy of 86% with a loss of 43%. Each uses batch 69 of the total training data stopping at epoch 30 from epoch 100. The results of field testing on the application of rice and chili disease detection on 20 images of rice and chili plants can detect Rice Neck Blast disease with a probability of 75% to 100% and Rice Hispa with a probability of 97% to 100%. It can also detect chili plant diseases such as Chili Yellowish with a probability of 83%, Chili Leaf Spot with a probability of 99%, Chili Whitefly with a probability of 91% to 95, Chili Healthy with a probability of 78% to 99%, and Chili Leaf Curl with a probability 75 to 76%. The probability obtained varies according to how likely damage is to rice and chili plants. CNN with the Mobile Net version one model and the Sequential model can extract and classify images so that it has maximum information processing capabilities. This research can make it easier to help farmers identify diseases that attack their rice and chili plants.
DESIGN AND DEVELOPMENT OF AN INTERNAL QUALITY AUDIT INFORMATION SYSTEM BASED PPEPP CYCLE Yani, Ahmad; Bakti, Lalu Darmawan; Akbar, Ardiyallah; Imran, Bahtiar
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4729

Abstract

The Mataram University of Technology Quality Assurance Institute already has and has established national education standards plus the standards set by universities following Permendikbud number 3 of 2020. However, there are problems with the implementation of Internal Quality Audits, where the implementation of internal quality audits is very less effective and efficient, good in terms of time, cost, and energy. This is because the Mataram University of Technology Quality Assurance Institute only has 3 auditors to audit 12 study programs in one year and even spends two months in a row. This is an important concern for researchers to build and produce an internal quality audit information system application program that can help implement the internal quality audit process carried out by the Mataram University of Technology Quality Assurance Institute. The design of the internal quality audit information system was carried out using the prototyping method. The application of the prototyping method in system design will make information system builders better and more structured. The internal quality audit information system was built using the PHP programming language with the CodeIgniter framework and MySQL as the database and implementing Code-View-Controller (MVC). The main objective of this research is to produce an internal quality audit information system so that it can assist the Mataram University of Technology Quality Assurance Institute in documenting and optimizing higher education quality management in a planned and sustainable manner following the PPEPP cycle
SISTEM INFORMASI PENJUALAN ONLINE (E-COMMERCE) BERBASIS WEB PADA TOKO MATAHARI PRAYA Mutaqin, Zaenul; Imran, Bahtiar; Rosida, Sri
Journal Computer and Technology Vol. 1 No. 2 (2023): Desember 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v1i2.46

Abstract

E-commerce merupakan suatu kontak transaksi perdagangan antara penjual dan pembeli dengan menggunakan media internet. Keuntungan yang diperoleh dengan menggunakan transaksi melalui e-commerce adalah untuk meningkatkan pendapatan dengan menggunakan penjualan online yang biayanya lebih murah dan lebih epektif. Adapun sistem yang digunakan adalah PHP, MySQL,XAMPP yang berbasis web. Sistem Informasi penjualan online berbasis web pada Toko Matahari Praya. Hasil penelitian menunjukan bahwa perancangan sistem informasi penjualan online berbasis web ini dapat membantu konsumen dalam mengakses informasi mengenai produk yang dijual dan dalam melakukan pemesanan produk, mengimplementasikan sistem informasi yang meliputi implementasi perangkat lunak perangkat keras, basis data serta antarmuka dari aplikasi yang dihasilkan.
SISTEM PAKAR DIAGNOSIS PENYAKIT PADA TANAMAN KACANG HIJAU BERBASIS WEB MENGGUNAKAN METODE DEMPSTER SHAFER Hamim, Lutfi; Imran, Bahtiar; Akbar, Ardiyallah
Journal Computer and Technology Vol. 1 No. 1 (2023): Juli 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v1i1.77

Abstract

Kacang hijau merupakan salah satu tanaman kacang-kacangan yang penting di Indonesia karena memiliki banyak gizi dan sebagai sumber pangan berprotein nabati tinggi. Namun dalam perkembangannya banyak pula tanaman kacang hijau yang terserang berbagai macam penyakit apalagi pada saat musim penghujan. Ketersediaan Waktu penyuluhan pun minim disebabkan banyaknya lokasi yang harus dikunjungi.Oleh karena itu perlu adanya sistem pakar yang mampu memberikan informasi tentang penyakit penyakit tanaman kacang hijau dan solusi permasalahannya. Penelitian ini bertujuan untuk membangun sistem pakar yang dapat mendiagnosa 3 jenis penyakit pada tanaman Kacang Hijau dari 17 gejala dengan menggunakan metode Dempster-Shafer berbasis website. Hasil uji coba menunjukkan bahwa sistem pakar ini memiliki tingkat akurasi diagnosa sebesar 80%, yang menunjukkan efektivitasnya dalam membantu mengidentifikasi jenis penyakit yang menyerang tanaman kacang hijau. Metode Dempster-Shafer yang digunakan dalam sistem ini memungkinkan penggabungan informasi dari berbagai gejala penyakit, sehingga memberikan hasil diagnosa yang lebih tepat dan akurat.
SISTEM PAKAR DIAGNOSA PENYAKIT MANDUL PADA PRIA MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEBSITE Giardi, Muh Hamzah Andung; Imran, Bahtiar; Suryadi, Emi
Journal Computer and Technology Vol. 1 No. 1 (2023): Juli 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v1i1.82

Abstract

Penyakit mandul pada pria merupakan kondisi medis yang mempengaruhi kemampuan pria untuk memproduksi sperma atau memiliki keturunan. Diagnosa dini dan tepat sangat penting dalam pengelolaan penyakit ini agar dapat memberikan perawatan yang tepat dan meningkatkan peluang keberhasilan reproduksi. Untuk mengatasi tantangan tersebut, sebuah sistem pakar berbasis website dikembangkan menggunakan metode Certainty Factor. Sistem pakar ini dirancang dengan antarmuka pengguna yang sederhana dan mudah digunakan, memungkinkan pengguna untuk memasukkan gejala dan riwayat kesehatan mereka. Kemudian, sistem akan melakukan proses diagnosa berdasarkan basis pengetahuan yang telah diimplementasikan dalam sistem dan memberikan rekomendasi diagnosa berdasarkan tingkat keyakinan.
Co-Authors AA Sudharmawan, AA Abba Suganda Girsang, Abba Suganda ahmad yani Ahmad Yani Akbar, Ardiyallah Akhmad Muzakka Alfian Hidayat Amirudin Kalbuadi Atika Zahra Nirmala Baihaki, Makmun Baiq Nonik Ria Riska Baiq Nonik Ria Riska Darmawan Bakti, Lalu Diki Hananta Firdaus Efendi, Muhamad Masjun Erfan Wahyudi erniwati, surni Fachrul Kurniawan Febri, Elin Febriani Giardi, Muh Hamzah Andung Hambali Hambali Hambali Hambali Hamim, Lutfi Hasan Basri Hidayatullah, Beni Ari Karim, Muh Nasirudin Karim, Muh. Nasirudin Karina Nurwijayanti Karya Gunawan Karya Gunawan Lalu Darmawan Bakti Lalu Darmawan Bakti, Lalu Darmawan Lalu Delsi Samsumar, M.Eng. M Zulpahmi M. Zulpahmi M. Zulpahmi Mahayadi, Mahayadi Makmun Baihaki Marroh, Zahrotul Isti’anah Maspaeni Maspaeni Moch Arief Soeleman, Moch Arief Muahidin, Zumratul Muh. Akshar Muhammad Masjun Efendi Muhammad Rijal Alfian Muhammad Zohri Mutaqin, Zaenul Muttaqin, Athaur Muzakka, Akhmad Nasirudin Karim, Muh Ndang, Rijalul Mujahidin Nining Putri Ningsih Nunung Rahmania Nurkholis, Lalu Moh. Pratama, Rifqy Hamdani Purnamasidi, Hanis Purwanto Purwanto Ramdan, Hendri Ricardus Anggi Pramunendar Riska, Baiq Nonik Ria Rosida, Sri Rudi Muslim Rudi Muslim Salman Salman Salman Salman Salman San Sudirman Saputra, Dede Haris Satriawan, Andre Selamet Riadi Selamet Riadi Soeleman, Moh. Arief Sriasih, Sriasih Subektiningsih Subektiningsih Subki, Ahmad Suharjito Suharjito, Suharjito Suhartono Supardianto Supardianto Surni Erniwati Suryadi, Emi Tahrir, Muhammad wahyuni, wenti ayu Zaeniah Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zahroni, Teguh Rizali Zenuddin, Z Zulpahmi, M Zulpahmi, M. Zulpan Hadi Zulpan Hadi