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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Tekno
Zaqi Kurniawan
Universitas Budi Luhur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Model Sistem Pendukung Keputusan Penentuan Strategi Peningkatan Kinerja Guru Menggunakan Metode Analytic Network Process Zaqi Kurniawan; Marimin Marimin; Rusdah Rusdah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 10, No 2 (2021): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v10i2.1130

Abstract

All parties are aware that teacher performance is directly proportional to improving the quality of education. Not a few teachers work under predetermined work standards, conditions like this are caused by low work enthusiasm which results in decreased performance. If we observer the passion of work in the form of sine graph which one day will meet a saturation point if there are no preventive and curative efforts eithers form himself or guidance form his superior. One of the efforts taken is to impose teacher performance assessment to ensure a quality learning process at levels of education. Teacher performance appraisal needs to be carried out so that the functions and duties in the functional teacher positions and duties in the functional teacher positions are carried out by the applicable rules and code of ethics. On that basis, a decision support system was created using the Analytical Network Process method which can determine teacher performance improvement strategies, based on objective performance appraisals and make decisions that become more efficient.
Sistem Pendukung Keputusan Penilaian Kinerja Guru untuk Rekomendasi Guru Tetap Berbasis Balanced Scorecard dengan Pendekatan Analytic Network Process Marimin Marimin; Zaqi Kurniawan; Rusdah Rusdah
Prosiding SISFOTEK Vol 3 No 1 (2019): SISFOTEK 2019
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1077.614 KB)

Abstract

Teacher Performance Assessment guarantees a quality learning process at all levels of education. Educational supervision will be carried out with the aim to improve the quality of teaching / teacher so that the competitiveness of students studying at the school will increase towards a better direction. Supervision assessment is a class visit technique to obtain data about the actual situation regarding the ability and skills of teachers in teaching and mastery of class. To determine the teacher's performance, one approach can be done using the Balanced Source card. The determination of teacher performance is then processed using Analytic Network Process-based modeling to improve teacher evaluation criteria that are still low. With the help of Super Decision software, a decision support system was created in determining teacher performance. The results of this study are the recommendations of permanent teachers in Junior High Schools, High Schools, Vocational Schools Yadika 12 Depok based on performance to be objective and make more efficient decisions.
PERBANDINGAN METODE BALANCED SCORECARD DAN NAÏVE BAYES DALAM PREDIKSI DAN REKOMENDASI PADA PENILAIAN KINERJA GURU (STUDI KASUS : SMK YADIKA 12 DEPOK) Kurniawan, Zaqi; Indra
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.230

Abstract

Teacher Performance Assessment guarantees a quality learning process at all levels of education. Educational supervision will be carried out with the aim to improve the quality of teaching / teacher so that the competitiveness of students studying at the school will increase towards a better direction. Supervision assessment is a class visit technique to obtain data about the actual situation regarding the ability and skills of teachers in teaching and mastery of class. To determine the teacher's performance, one approach can be done using the Balanced Scorecard approach and Naïve Bayes classification. The determination of teacher performance is then processed using Analytic Network Process-based modeling to improve teacher evaluation criteria that are still low. With the help of Super Decision software, a decision support system was created in determining teacher performance. The results of this study are the recommendations of permanent teachers in Junior High Schools, High Schools, and Vocational Schools Yadika 12 Depok based on performance to be objective and make more efficient decisions.
Mengoptimalkan Mutu Pembelajaran Melalui Workshop Penggunaan LMS Moodle untuk Dewan Guru SMK Yayasan Abdi Karya 5 Pondok Aren Kurniawan, Zaqi; Rahdiana, Ikhsan
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 4 No 1 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v4i1.5150

Abstract

Tujuan kegiatan pengabdian pada masyarakat ini adalah mengenalkan aplikasi e-learning berbasis Learning Management System (LMS) kepada dewan guru SMK Yadika 5 Pondok Aren. Keterampilan dalam menggunakan media sosial yang dimiliki oleh guru diharapkan mempermudah proses pembelajaran dengan menggunakan LMS Moodle. Metode yang dilakukan dalam pengabdian masyarakat ini adalah dalam bentuk ceramah, diskusi, dan praktek. Kegiatan workshop ini dilakukan selama 2 hari dimana hari pertama kegiatan lebih fokus dalam memberikan informasi mengenai pemanfaatan e-learning, pengenalan moodle, proses mendaftar moodle, sampai pada pengenalan fitur-fitur dan keunggulan moodle. Pada hari kedua, pelatihan fokus pada pengemasan konten pembelajaran, melakukan praktek dan simulasi proses pembelajaran dengan memanfaatkan fitur-fitur yang ada pada moodle.
Machine Learning Approach for Early Diagnosis of Dyslexia Among Primary School Children: A Scoping Review and Model Development Kurniawan, Zaqi; Tiaharyadini, Rizka
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.30614

Abstract

Dyslexia, a prevalent learning disorder among primary school children, often goes undetected until later stages, hindering academic progress and socio-emotional development. Early diagnosis is crucial for effective intervention. Machine Learning (ML) offers promise in developing accurate diagnostic tools. However, there's a scarcity of comprehensive reviews focusing on ML approaches for dyslexia diagnosis in this demographic. In this scoping review, we consolidate existing literature and present the development of a novel ML model that was customized for early dyslexia diagnosis. Utilizing Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest. The comparative analysis of ML methods for dyslexia detection in elementary school children reveals distinct strengths. Decision Tree shows robust precision: 92.31% for dyslexia-prone, 90.62% for diagnosed dyslexia, and 86.67% for no dyslexia detected, with corresponding high recall values of 90.57%, 87.88%, and 100%, respectively. KNN excels with an overall accuracy of 94.00% and perfect precision for undetected dyslexia (100%), with high precision and recall for dyslexia-prone and diagnosed dyslexia. Logistic Regression highlights significant predictors and achieves precision of 95.38% for dyslexia-prone and 88.24% for diagnosed dyslexia, with recall rates of 93.34% and 90.91%, respectively. Naive Bayes exhibits outstanding precision for no dyslexia and dyslexia-prone categories (100%), with slightly lower precision for diagnosed dyslexia (82.5%), but perfect recall for undetected and diagnosed dyslexia. Random Forest demonstrates balanced performance with precision ranging from 91.18% to 94.23% and recall from 92.31% to 93.94%, achieving an overall accuracy of 93.00%. These results underscore ML's potential in enabling early dyslexia detection, facilitating timely interventions to improve outcomes for affected children and advancing dyslexia diagnosis.
Data Cleaning Metode Participatory Action Research untuk Karyawan PT. Matahari Department Store Kurniawan, Zaqi; Tiaharyadini, Rizka; Anif, Muhammad; Rahdiana, Ikhsan; Jonathan, Jeremy
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 5 No 2 (2024): Desember 2024:
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v5i2.6286

Abstract

Accurate and efficient data management is a critical requirement in the digital age, especially in the retail sector that relies on data analytics to support decision-making. However, many employees face obstacles in understanding and applying data cleaning techniques that hinder the optimization of their analytical performance. This community service activity aims to improve the ability of PT Matahari Department Store employees to perform data cleaning using Microsoft Excel. The Participatory Action Research (PAR) approach is applied so that participants can actively participate in every stage of the abdimas carried out. The results of the activity showed a significant increase in participants' understanding and skills with the average post-test score increasing by 20% compared to the pre-test. The greatest improvement was achieved in the use of the Power Query feature, with a 40% increase in understanding. Program evaluation through questionnaires and interviews showed that the relevant training structure and interactive tools played a major role in the success of this activity. The conclusion of this activity is that hands-on training designed with a PAR approach is effective in overcoming employees' technical obstacles and improving their analytical performance. Recommendations are given to continue similar programs with a focus on data automation and advanced analytics to support work efficiency in the retail sector.
Predicting Catfish Growth and Feed Efficiency in Using Decision Tree and Support Vector Regression Kurniawan, Zaqi; Tiaharyadini, Rizka
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32889

Abstract

Catfish farming has a key part in maintaining the economy of Poris Plawad Utara, Cipondoh, Tangerang where many farmers depend on it as their primary source of income. However, poor feed management creates considerable obstacles as overfeeding leads to higher expences and enviromental issues while underfeeding inhibits fish growth. Traditional methods for identifiying ideal feed amounts rely on manual observation, which often leads in irregular growth rates and feed loss. Despite the necessity of effective feed utilization, there is a paucity of powerful predictive techniques available to enable farmers accurately forecast feed demands and fish growth. There, we employ machine learning approaches including Decision Tree and Support Vector Regression (SVR), to predict catfish development and feed efficiency based on several environmental parameters such as water temperature, pH levels, and oxygen concentration. The algorithm we used was trained using data acquired from catfish farm in Poris Plawad Utara, comprising 3 month of feeding and growth records. The results of the analysis demonstrate that while Support Vector Regression (SVR) and Decision Trees perform well in stable environments, they have trouble handling environmental changes. Accuracy is impacted by feed management and environmental stability. More variables and an intricate machine learning strategy are required for better performance. While SVR works well in stable systems, complicated dynamics require adaptations. These results show that feed efficiency and fish development may be grately increased by incorporating machine learning into catfish farming operations. This methodology provides farmers with data-driven solutions that maximizes the efficiency of aquaculture and sustainability.
Peningkatan Minat dan Kesiapan Akademik Siswa melalui Simulasi Edukatif Budi Luhur College Rizka Tiaharyadini; Zaqi Kurniawan; Windhy Widhyanty
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 6 No 1 (2025): Juni 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v6i1.6671

Abstract

The transition from high school to higher education is a critical phase that demands academic readiness, mental preparedness, and strong student motivation. However, many high school students still have limited understanding of university life and the realities of the academic process. The lack of exposure to campus environments contributes to low motivation and inadequate preparation for continuing their studies, highlighting the need for a more contextual and interactive educational approach. In response, the Budi Luhur College 2025 program was designed to provide direct experience through lecture simulations, computer laboratory practicums, and faculty excursions. The program engaged 160 Grade XI students from SMA Budi Luhur, conducted over 14 sessions from February 14 to May 30, 2025. Evaluation results showed a significant increase in student satisfaction, with the number of students rating the program as “good/very good” rising from 38 at the beginning to 47 by the end. Additionally, 82% of participants reported improved understanding of campus life, and 75% stated they felt more prepared to pursue higher education. The most influential satisfaction factors were direct lecture experience (31.2%) and faculty excursions (31.2%). These findings suggest that the participatory and simulation-based approach used in the program is effective in enhancing students’ academic orientation and motivation. This success presents opportunities for future program expansion, either as an annual institutional initiative or as a sustainable collaborative community service model.
Co-Authors Indra Jonathan, Jeremy Marimin , Muhammad Anif Rahdiana, Ikhsan Rizka Tiaharyadini Rusdah Rusdah Tiaharyadini, Rizka Windhy Widhyanty