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All Journal CommIT (Communication & Information Technology) Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Indonesian Journal of Electrical Engineering and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Iskandar Fitri, Iskandar
Bina Nusantara University Master of Information Technology
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy

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Application of Spinal Disorders Detection on X-Ray Images Using Segmentation and K-Means Clustering Khairul, Muhammad; Fauziah, Fauziah; Fitri, Iskandar
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 6, No 3 (2022): July-September
Publisher : KITA Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v6i3.456

Abstract

There are three types of spinal disorders, namely kyphosis, lordosis, and scoliosis. To find out spinal disorders, it is necessary to carry out X-rays from an early age. Spinal disorders are not only found in children but can be found in adolescents, adults, and the elderly. Along with the times, making information technology more sophisticated is the advancement of image processing technology. Image processing can help in the medical field to analyze X-ray results to diagnose internal disorders or diseases. This study makes an application for the detection of spinal disorders with several methods of image segmentation processes and using the k-means clustering algorithm on x-ray images of spinal disorders. This segmentation image processing stage requires five stages of processing including cropping, resizing, median filter, histogram equalization, thresholding, and binary edges, and k-means clustering process as a comparison. This application is expected to be useful in knowing the difference between spinal disorders of lordosis, kyphosis, and scoliosis
Perancangan Aplikasi Game IQ Test dengan Mengimplementasikan Linear Congruent Method (LCM) Syiamtoni, Eky Pambudi; Fitri, Iskandar; Ningsih, Sari
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 9, No 2 (2021)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.327 KB) | DOI: 10.26418/justin.v9i2.43329

Abstract

Dengan dilakukannya penelitian mengenai perancangan aplikasi Game IQ Test ini, diharapkan bisa berfungsi sebagai media alternatif yang pada umumnya media pengujiannya masih banyak yang menggunakan kertas, tetapi hal itu kurang efesien karena bisa berdampak buruk pada lingkungan dan mengancam kelestarian hutan, oleh karena itu dengan kemajuan teknologi bisa dimanfaatkan untuk media alternatif dari pemakaian kertas. Perancangan aplikasi Game IQ Test bisa lebih efesien untuk semua pengguna dan lebih ramah untuk lingkungan, karena aplikasi Game IQ Test ini sifatnya mobile dengan menggunakan tools construct 2. Dalam perancangannya dibuat system acak soal, system yang digunakan untuk mengacak soal yaitu dengan memakai Linear Congruent Method atau biasa disebut LCM, LCM adalah membangkit bilangan acak, biasa sering digunakan pada pemprograman computer, sebagian orang yang memakai metode LCM ini pada aplikasinya hanya sebagai pengacak soal agar setiap pengguna mendapatkan bentuk soal yang berbeda. Dari hasil pengujian pada aplikasi Game IQ Test mendapatkan presentase nilai rata - rata 88% yang menjawab setuju dari penggunaan skala likert.
PROTOTIPE SISTEM MONITORING PENDETEKSI KEBAKARAN MENGGUNAKAN FITUR LOOPING Zikrullah, Alhadi Putra; Tamara, Rima; Fitri, Iskandar
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 1 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i1.2536

Abstract

Kebakaran merupakan peristiwa yang tidak dikehendaki oleh manusia.Hal ini mengarah kepada pentingnya menjaga suatu ruangan dari bahaya kebakaran yang berakibat fatal,yang mana kejadian ini dapat terjadi tidak mengenal tempat dan waktu, bisa terjadi dimana saja dan kapan saja.Kebakaran dapat mengakibatkan kerugian material atau kerugian korban jiwa.Untuk itu di perlukan alat yang bisa mendeteksi kebarakaran api,asap dan gas.Perancangan alat pendeteksi kebakaran ini adalah rancangan sistem yang terhubung melalui jaringan internet dan dapat memberikan informasi jika terjadi adanya indikasi kebakaran kepada pihak terkait melalui Monitoring yang diharapkan dapat mencegah terjadinya kebakaran dalam skala besar.Alat pendeteksi kebakaran ini menggunakan mikrokontroller yang sudah di lengkapi dengan beberapa sensor yaitu dengan Flame sensor,MQ-2 dan MQ-7.Jika Flame sensor terdeteksi adanya api dengan jarak 5cm dengan besarnya api 85’c maka alarm Buzzer akan berbunyi dan akan mengeluarkan air.NodeMCU akan mengirim perintah ke relay dan relay akan menyalahkan pompa.Jika MQ-2 dan MQ-7 mendeteksi adanya asap dengan kepadatan melebihi angka 770 PPM dan gas 658%,maka dihalaman Monitoring akan menampilkan angka yang sama dan akurat.Kesimpulan dari penelitian ini adalah dengan adanya sistem pendeteksi kebakaran,Prototype ini menggunakan beberapa sensor seperti Flame sensor,MQ-2,dan MQ-7 yang dapat menunjang alat kerja seacara sistematis dan user dapat dapat mengetahui seberapa bahaya ruangan tersebut dengan melihatnya ditampilan pada halaman monitoring.
Diagnosa Penyakit Tulang Belakang Menggunakan Metode Forward Chaining dan Certainty Factor Jayanti, Riza Dwi; Rahman, Ben; Fitri, Iskandar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3497

Abstract

In the field of medicine, especially orthopedics, there are several types of spinal diseases including scoliosis, lordosis, kyphosis, and spondylosis. Therefore, an expert system is needed to diagnose spinal diseases. Based on this problem, we performed an exponential comparison of the two hybrid methods. With the final value of the combined forward chaining & naive bayes method of 8.89, and 9.40 the highest final value of the combined forward chaining method and certainty factor. So that in this research, forward chaining and certainty factor methods are used which are designed based on a website, using Sublime Text 3 programming tools and PHP and MySQL as databases. From the results of application testing and manual calculations with 30 sample data, it was concluded that 7 users or about 23% entered the level of confidence in the possibility of developing spinal disease and 23 users or 77% of the total testing stated at the level of confidence in the probability of developing spinal disease with a value of the highest confidence of 88.5% in spondylosis disease.
Sistem Pakar Delirium Pasien COVID-19 Pada Lansia Menggunakan Metode Certainty Factor dan Forward Chaining Ernawati, Ernawati; Hidayatullah, Deny; Fitri, Iskandar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3503

Abstract

In this study, it is related to an expert system for detecting delirium in COVID-19 patients which has never been done in Indonesia in previous studies, related to the method used by researchers to compare 2 hybrid methods, namely the Certanty Factor method with the Forward Chaining method compared to the Bayes method and Forward Chaining . From the results of the comparison, a higher confidence value is obtained, namely the certainty factor and forward chaining as a method in making an expert system application for detecting delirium in elderly COVOD-19 patients. From the results of the application test, which is compared with the calculation results, it is known that from the 20 patient samples, 70% of the samples stated Possibility and 30% stated  Most Likely from the diagnosed disease. The certainty factor technique is applied to calculate the certainty value of a fact or rule and the forward chain method is used to draw conclusions that will help diagnose a disease.
Application of Spinal Disorders Detection on X-Ray Images Using Segmentation and K-Means Clustering Khairul, Muhammad; Fauziah, Fauziah; Fitri, Iskandar
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 6 No 3 (2022): JULY-SEPTEMBER 2022
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v6i3.456

Abstract

There are three types of spinal disorders, namely kyphosis, lordosis, and scoliosis. To find out spinal disorders, it is necessary to carry out X-rays from an early age. Spinal disorders are not only found in children but can be found in adolescents, adults, and the elderly. Along with the times, making information technology more sophisticated is the advancement of image processing technology. Image processing can help in the medical field to analyze X-ray results to diagnose internal disorders or diseases. This study makes an application for the detection of spinal disorders with several methods of image segmentation processes and using the k-means clustering algorithm on x-ray images of spinal disorders. This segmentation image processing stage requires five stages of processing including cropping, resizing, median filter, histogram equalization, thresholding, and binary edges, and k-means clustering process as a comparison. This application is expected to be useful in knowing the difference between spinal disorders of lordosis, kyphosis, and scoliosis
Catfish Fry Detection and Counting Using YOLO Algorithm Takyudin, Takyudin; Fitri, Iskandar; Yuhandri, Yuhandri
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6746

Abstract

The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 % Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.
Development and modification Sobel edge detection in tuberculosis X-ray images Devita, Retno; Fitri, Iskandar; Yuhandri, Yuhandri; Yani, Finny Fitry
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1191-1200

Abstract

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.
Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography Rizki, Syafrika Deni; Yuhandri, Yuhandri; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1750-1758

Abstract

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.
Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes Aziz, Abdul; Fauziah, F; Fitri, Iskandar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.381

Abstract

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naïve Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.
Co-Authors Abdul Aziz Abdullah, Mohammad Aldinugroho Abdullah, Syamsun Nahar Agus Iskandar Albaar Rubhasy Aldisa, Rima Tamara Alhadi, Alhadi Andrianingsih Ardiansyah, Aldi Aris Gunaryati, Aris Arundy, Vicky Agnes Azamta, Firnas Benrahman Burhani, Hanif Rahman Cahyadi, Iman Deny Hidayatullah Devita, Retno Endah Tri Esti Handayani Ernawati Ernawati Fauziah Fauziah Fauziah, F Finny Fitry Yani Furqaansyah, Yoga Gunaryarti, Aris Gunaryati, Agus Haryono, Muklis Hasbulloh, Hasbulloh Herlan, Agus Humansyah, Hanggita Ahmad Iriandi, Aditya Nuryudha Irsandi, Johar Saputra Jayanti, Riza Dwi Khuzaifi, Ahmad Lesmana, Delles Mardia, Ahmad Muslih Mardiani, Eri Muchlis, Muhammad Muttaqin Muhammad Khairul Muhammad Ridwan Muhammad, Fauzan Nehe, Kurniadin Novi Dian Nathasia Nurhayati Nurhayati Oka Saputra Pangestu, Danang Aji Prayogi, Bayu Setyo Putra, Muhammad Aldi Aditia Putra, Wisnu Andika Rahman, Ben Rini Nuraini, Rini Rohadi, Chairil Sari Ningsih Sari Ningsih Shofi, Niam Sholihatih, Ira Diana Silfia Andini, Silfia Situmorang, Daud Sopiyan, Muhamad Sumarhadi, Sumarhadi Sumijan Sumijan Supriyanto Supriyanto Suryadi Suryadi Syafii, Muhammad Faiz Syafrika Deni Rizki, Syafrika Deni Syiamtoni, Eky Pambudi Takyudin, Takyudin Taslim, Denis Utomo, Budi Tri Wilianto Wilianto Yauma, Alfath Yudhi Setiawan Yuhandri Yuhandri, Yuhandri Zayana, Muhammad Rafiq Zikrullah, Alhadi Putra