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Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University Muhammad Darwis; Liyando Hermawan Hasibuan; Mochammad Firmansyah; Nur Ahady; Rizka Tiaharyadini
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.848

This study aims to determine the characteristics of students who are likely to graduate or drop out (DO) in the management department of the National University, Jakarta. The study was conducted by implementing the K-Means algorithm, where each data is grouped according to the closest distance to the centroid. Determination of Cluster C1 graduate or C2 drop out is based on the attributes of status of students (active, leave, out and non-active), educational status (graduated or DO), GPA, total credits taken and length of study. To facilitate the clustering process, Orange tools are used that provide K-Means algorithm features. The total data input in this study were 1988 students from various classes. As a result, a pattern or mapping of graduated or DO students was found based on the attributes mentioned earlier. Testing the results of this cluster with the silhouette method, by measuring the distance between cluster members, both C1 and C2, showed good Silhouetter value, reaching 85%. The management department, National University can use the results of this study to predict the graduation of their students.

Penerapan Metode Analytical Hierarchy Process Dan Simple Additive Weighting pada Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Azizah Febriani; Anita Diana; Rizka Tiaharyadini; Atik Ariesta
ikraith-informatika Vol 5 No 3 (2021): IKRAITH-INFORMATIKA Vol 5 No 3 November 2021
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1178.705 KB)

Penilaian kinerja karyawan menempati peran penting untuk kelancaran perusahaan,terutama dengan pemilihan karyawan terbaiknya. Studi kasus penelitian ini bertempatpada PT. Ngampooz Pintar Sejahtera, dimana proses berjalannya, belum mempunyaiproses penilaian kinerja karyawan, sehingga mengalami beberapa kendala. Kendalatersebut antara lain belum adanya metode yang tepat dan nilai bobot kriteria dalamprosespemilihan karyawan terbaik, serta belum adanya informasi laporan yang dibutuhkansehingga berdampak kurangnya informasi kinerja karyawan untuk pimpinan. Untukmembantu mengatasi kendala ini,maka dibutuhkan sebuah system SPKuntuk pemilihankaryawan terbaik. Metode yang digunakan adalah Analytical Hierarchy Process (AHP)dan Simple Additive Weighting (SAW). Metode AHP digunakan karena merupakan salahsatu metode populer untuk mencari nilai pembobotan untuk setiap kriteria yangditetapkan. Metode kedua memiliki proses hitung yang sederhana, dapat menampilkanprioritas urutan alternatif berupa ranking tertinggi sampai terendah, mudahdiimplementasikan, dan menggunakan konsep pembobotan dalam perhitungannya yaituSimple Additive Weighting (SAW). Penelitian ini bertujuan untuk menghasilkan sebuahSPK yang dapat mengurangi terjadinya kesalahan dalam pencatatan atau penghitungan,meningkatkan efektifitas dalam pengolahan data, memudahkan dalam pemilihan danpembuatan laporan hasil kinerja karyawan yang cepat dan akurat. SPK ini mempercepatproses pemilihan karyawan terbaik serta membantu perusahaan untuk prosesperangkingan dalam pemilihan karyawan terbaik dengan tepat.

Penerapan Metode Analytical Hierarchy Process Dan Simple Additive Weighting pada Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Azizah Febriani; Anita Diana; Rizka Tiaharyadini; Atik Ariesta
ikraith-informatika Vol 5 No 3 (2021): IKRAITH-INFORMATIKA Vol 5 No 3 November 2021
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1178.705 KB)

Penilaian kinerja karyawan menempati peran penting untuk kelancaran perusahaan,terutama dengan pemilihan karyawan terbaiknya. Studi kasus penelitian ini bertempatpada PT. Ngampooz Pintar Sejahtera, dimana proses berjalannya, belum mempunyaiproses penilaian kinerja karyawan, sehingga mengalami beberapa kendala. Kendalatersebut antara lain belum adanya metode yang tepat dan nilai bobot kriteria dalamprosespemilihan karyawan terbaik, serta belum adanya informasi laporan yang dibutuhkansehingga berdampak kurangnya informasi kinerja karyawan untuk pimpinan. Untukmembantu mengatasi kendala ini,maka dibutuhkan sebuah system SPKuntuk pemilihankaryawan terbaik. Metode yang digunakan adalah Analytical Hierarchy Process (AHP)dan Simple Additive Weighting (SAW). Metode AHP digunakan karena merupakan salahsatu metode populer untuk mencari nilai pembobotan untuk setiap kriteria yangditetapkan. Metode kedua memiliki proses hitung yang sederhana, dapat menampilkanprioritas urutan alternatif berupa ranking tertinggi sampai terendah, mudahdiimplementasikan, dan menggunakan konsep pembobotan dalam perhitungannya yaituSimple Additive Weighting (SAW). Penelitian ini bertujuan untuk menghasilkan sebuahSPK yang dapat mengurangi terjadinya kesalahan dalam pencatatan atau penghitungan,meningkatkan efektifitas dalam pengolahan data, memudahkan dalam pemilihan danpembuatan laporan hasil kinerja karyawan yang cepat dan akurat. SPK ini mempercepatproses pemilihan karyawan terbaik serta membantu perusahaan untuk prosesperangkingan dalam pemilihan karyawan terbaik dengan tepat.

DevOps, Continuous Integration and Continuous Deployment Methods for Software Deployment Automation Istifarulah, Mochamad Hanif Rifa'i; Tiaharyadini, Rizka
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1751

In the fast-paced landscape of software development, the need for efficient, reliable, and rapid deployment processes has become paramount. Manual deployment processes often lead to inefficiencies, errors, and delays, impacting the overall agility and reliability of software delivery. DevOps, as a cultural and collaborative approach, plays a central role in orchestrating the synergy between development and operations teams, fostering a shared responsibility for the entire software delivery lifecycle. Continuous Integration is a fundamental DevOps practice that involves regularly integrating code changes into a shared repository, triggering automated builds and tests. Continuous Deployment complements Continuous Integration by automating the release and deployment of validated code changes into production environments. The purpose of this research is to create a software deployment automation system to make it easier and reliable for organizations to deploy software. In conclusion, the results of this research show that by adopting DevOps, Continuous Integration, and Continuous Deployment, organizations can achieve enhanced collaboration, shortened release cycles, increased deployment frequency, consistent deployment, and improved overall software quality.

Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education Kurniawan, Zaqi; Tiaharyadini, Rizka
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1950

The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.

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

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

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

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.

Trend Analysis and Prediction of Violence Against Women and Children Cases in Jakarta Based on the Victim’s Education Level Using ARIMA and SARIMA Method Kurniawan, Zaqi; Tiaharyadini, Rizka; Wibowo, Arief; Rusdah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2349

Violence against women and children remains a critical social issue in Jakarta, Indonesia, where densely populated urban areas often correlate with increased risks of domestic abuse. The urgency of addressing this problem lies in its direct impact on public health, education, and community well-being. This study uses time series prediction models to examine and anticipate trends in the number of reported incidents of violence against women and children in Jakarta. Using publicly accessible data from Jakarta Open Data and the National Commission for the Protection of Women and Children, we applied the ARIMA and SARIMA Models. Key variables included in the dataset are the data period, education level, and total number of victims Using three performance indicators—MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error)—to assess model accuracy the ARIMA model performed better than the SARIMA model. SARIMA recorded an RMSE of 80.26, an MAE of 66.21, and an undefined MAPE because of zero values in the real data, while ARIMA specifically obtained an RMSE of 32.22, an MAE of 32.09, and a MAPE of 5.19%. These results suggest that the non-seasonal ARIMA model is more suitable for this dataset. The study contributes to policy planning and early intervention strategies by offering a data-driven approach to predicting trends in violence within urban contexts.

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