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All Journal Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Seminar Nasional Teknologi Informasi Komunikasi dan Industri Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah Jurnal Pengabdian Masyarakat Multidisiplin Journal of Applied Computer Science and Technology (JACOST) Technologica Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Proceeding Applied Business and Engineering Conference SATIN - Sains dan Teknologi Informasi
Mardhiah Fadhli, Mardhiah
Jurusan Teknik Komputer Politeknik Caltex Riau Pekanbaru
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INTEGRASI NAIVE BAYES DAN ITEM-BASED COLLABORATIVE FILTERING DALAM SISTEM PEMETAAN KOMPETENSI MAHASISWA Nurmalasari, Dini; Fadhli, Mardhiah; Yuli Fitrisia, Yuli Fitrisia; Yuliantoro, Heri R
Jurnal Komputer Terapan Vol 11 No 1 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i1.6612

Abstract

Preparing a strong portfolio is a crucial aspect for students in entering the workforce, one of which can be achieved through participation in various competitions. However, selecting competitions that align with student competencies remains a challenge due to the abundance of competition information, diversity in student interests and abilities, and limitations in budget, time, and resources. This study develops a recommendation system based on a Hybrid Recommendation System designed to map student competencies to relevant competition types. The system integrates the Naive Bayes method to classify student competencies and Item-Based Collaborative Filtering to calculate similarities between competition types based on other users’ preferences. The system is developed incrementally using the waterfall approach, including the stages of planning, analysis, design, implementation, and testing. The model follows standard machine learning workflows, comprising data collection, exploration and preprocessing, model building, performance evaluation, and method integration. The research data includes student profiles, competencies, and competition preferences collected through surveys and internal databases. Evaluation results indicate that the system successfully provides relevant competition recommendations with an accuracy rate of 70%. These results demonstrate the system’s contribution in assisting students to select competitions that match their competencies, presented in a user-friendly web-based application.
Pengujian Kualitas Coding Pada Aplikasi Bank Sampah DLHK Kota Pekanbaru Menggunakan Code Smell Tools fadhli, mardhiah; Yuli Fitrisia; Nurmalasari, Dini
Jurnal Komputer Terapan Vol 10 No 1 (2024): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v10i1.6211

Abstract

Kualitas dari kode program akan mempengaruhi kemampuan perangkat lunak untuk dapat mudah dimodifikasi dan dikembangkan serta dipelihara. Code smells merupakan suatu karakteristik dari perangkat lunak yang mengindikasikan permasalahan pada kode dan desain perangkat lunak yang mengakibatkan perangkat lunak sulit untuk dikembangkan dan dilakukan pemeliharaan. Deteksi code smell perlu dilakukan agar dalam pengembangan kedepannya aplikasi dapat lebih mudah dimodifikasi dan dikembangkan. Deteksi code smell dalam sebuah aplikasi dapat membantu programmer untuk mengidentifikasi adanya rancangan kode program yang dapat menyulitkan kedepannya untuk dilakukan modifikasi dan pengembangan. Pendeteksian Code Smell pada aplikasi Bank Sampah DLHK Kota Pekanbaru dilakukan karena adanya permintaan kebutuhan untuk perbaikan dan penambahan fitur dari Bank Sampah DLHK Kota Pekanbaru. Permintaan perbaikan dan penambahan fitur pada aplikasi Bank Sampah DLHK Kota Pekanbaru dilakukan berdasarkan hasil evaluasi aplikasi sebelumnya yang dilakukan oleh pihak DLHK Kota Pekanbaru kepada 10 Bank Sampah Unit dan 25 Nasabah. Berdasarkan hasil evaluasi dan uji coba aplikasi, maka perlu dilakukan penyesuaian karena adanya ketidaksinkronisasian proses dengan mekanisme yang sedang berjalan dimasyarakat terhadap fitur pada aplikasi tersebut. Untuk memudahkan proses modifikasi program maka pendeteksian code smell pada aplikasi yang sudah ada perlu dilakukan, agar programmer dapat menjaga kualitas kode program menjadi lebih mudah untuk dikembangkan. Deteksi Code Smell dilakukan dengan menggunakan tool SonarQube. Hasil dari pengukuran dari dua aplikasi Bank Sampah adalah, pada aplikasi Basada berbasis mobile terdapat 126 Code smells dengan estimasi waktu perbaikan sekitar 2 jam 34 menit. Sedangkan pada aplikasi Basada berbasis website terdeteksi 25 Code smells dengan estimasi waktu perbaikan sekitar 25 menit.
KLASIFIKASI SUARA JANTUNG MENGGUNAKAN DEEP LEARNING INTEGRASI AI DALAM APLIKASI WEB UNTUK DETEKSI DINI GANGGUAN KARDIOVASKULAR nengsih, warnia; Elviyenti, Mona; Fadhli, Mardhiah; Aleanda, Galih; Utama, Nuradila
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6516

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection crucial to prevent more serious complications. One of the methods that can be used is heart sound analysis, which contains important information related to the physiological and pathological conditions of the heart. However, the manual analysis process by healthcare professionals requires specialized skills and may result in interpretation errors. Therefore, this research aims to develop an artificial intelligence-based system using Convolutional Neural Networks (CNN) to automate heart sound classification. This system allows users to upload heart sound recordings, which will then be processed and classified as Normal or Abnormal. The research process consists of several main stages, including data collection and preprocessing of heart sounds, development and training of the CNN model, implementation of the model into a web application, and testing and evaluation of the system using metrics such as accuracy, precision, and recall. The outcome of this research includes a deep learning model for heart sound classification. The developed system is expected to enhance the accuracy and efficiency of heart disease detection, reduce reliance on manual analysis, and serve as an artificial intelligence-based solution that can be integrated into healthcare services.
Comparative Analysis to Determine the Best Accuracy of Classification Methods Nengsih, Warnia; Fitrisia, Yuli; Fadhli, Mardhiah
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1128.134-141

Abstract

The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each classification method used. The object that becomes simulation in this research is the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have good accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%
PELATIHAN PEMBUATAN PERANGKAT AJAR INTERAKTIF MENGGUNAKAN MENTIMETER Yuli Fitrisia; Mardhiah Fadhli; Dini Nurmalasari; Wenda Novayani; Sugeng Purwantoro ESGS
Jurnal Pengabdian Masyarakat Multidisiplin Vol 5 No 3 (2022): Juni
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v5i3.2388

Abstract

Currently, the whole world is struggling to deal with the COVID-19 pandemic. During the pandemic, many things were affected and changed the habits of all mankind in the world, including the field of Education which changed drastically. Previously, the teaching and learning process was mostly done face-to-face in the same place or location, but now it has to be done online. During the online learning process, many problems arise from both the teacher and student. One of them is that students get bored quickly, lack of student involvement and participation in the teaching and learning process. This is a challenge for teachers how to create an interesting and interactive learning atmosphere. However, the obstacle faced by teachers is that not all teachers understand and know what media can be used to make the learning process interesting and interactive. Based on these problems, in this Pengabdian kepada Masyarakat activity, training activities for making teaching devices using a Mentimeter have been carried out for teachers of SMK Taruna Persada Dumai which was held on September 25, 2021 with a total of 16 participants. The purpose of this activity is to prepare an interesting and interactive teaching and learning process to increase student involvement and participation when the teaching and learning process is carried out online and to help partners have basic understanding and skills in using interactive learning applications. The result of this activity is an evaluation of activities that have been carried out through a questionnaire, it is found that the material presented can add insight to the teacher, in accordance with the expected competencies so that later it can be applied to the teaching and learning process. In addition, participants felt comfortable following the training because of the positive interaction between participants and presenters and the way the material was presented in an interesting way. Based on the results of the questionnaire, recommendations were also obtained for the next activity in the form of training in making learning videos and using other learning media to support the teaching and learning process.
IMPLEMENTASI SCL UNTUK MENAMBAH KOMPETENSI SISWA SMK DALAM MEMONITOR PROYEK IOT MELALUI PLATFORM BLYNK: SCL IMPLEMENTATION TO ADD VOCATIONAL SCHOOL STUDENT COMPETENCE IN MONITORING IOT PROJECTS THROUGH THE BLYNK PLATFORM Yoanda Alim Syahbana; Sugeng Purwantoro E.S.G.S; Memen Akbar; Wenda Novayani; Mardhiah Fadhli
Jurnal Pengabdian Masyarakat Multidisiplin Vol 6 No 3 (2023): Juni
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v6i3.3192

Abstract

SMK Taruna Persada Dumai is one of the vocational schools that participate in the SMK Center of Excellence program. This program focuses on developing specific skill competencies for SMK students. Competency development is prioritized on competencies that are aligned with the business world, industrial world, and world of work. Based on the evaluation of the previous year's Community service activities, the SMK Taruna Persada Dumai asked to continue the activity to increase its students’ competencies. The competency that will be taught in the 2022 Community service PSTRK is an introduction to the field of IoT which is currently developing. PSTRK implements Student-Centered Learning (SCL) to increase the competency of SMK students in monitoring IoT projects through the Blynk platform. Community service was held on September 6, 2022, from 9.00 am to 14.00 pm. This activity was attended by 20 students from the Department of Computer Network Engineering, SMKS Taruna Persada Dumai City. The given SCL module consists of 5 parts. The first part focuses on the small group discussion learning model in the introduction of processors, actuators, and sensors. Then, in the second part, the students simulated the LED and ESP32 circuits using the wokwi.com simulator. The third part is followed by a case study model of the LED series and NodeMCU 8266. Students’ enthusiasm for independent learning makes this third part take a long time. So, the fourth part in the form of colorful light control role-plays in the form of LED control via Blynk, and the fifth part in the form of a discovery learning model for reading DHT11 sensor data did not have time to work on it. As a solution, the Community service team left two sets of learning modules for students to work on independently later. At the end of the lesson, feedback from students was collected and the results showed their satisfaction with the material provided, the way it was presented, the quality of the modules, and the suitability of the material. The students also hope to be included in other Community service programs with different materials.
Co-Authors Afifah Afifah Syahirah Akbar, Memen Aleanda, Galih Ananda Tri Putra Annisa Marion Charly Sutanto Elviyenti, Mona Intan Sandra Yatana Saputri Jordan Abeth Teofilus Pasaribu Kartina Diah K. W. Kartini Diah KW M. Haris Wandra memen akbar Nabilah Nasution Nurmalasari, Dini Rifqi Amal Reza Risma Amelia Putri Sugeng Purwantoro E.S.G.S Surya, Ibnu Utama, Nuradila Warnia Nengsih Wenda Novayani Yoanda Alim Syahbana Yuli Fitrisia Yuli Fitrisia Yuli Fitrisia, Yuli Fitrisia Yuliantoro, Heri R