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Analysis of the Teacher's Role in Evaluation of Student Learning Performance Using the TOPSIS Model (Case Study of Smk Negeri 1 Lhokseumawe) arief rahman; Zahratul Fitri; Zulkifli Zulkifli; Mutammimul Ula; Bambang Suhendra
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6288

Analysis Teacher performance assessment is very decisive in the quality of teaching and learning, This has an effect at the high-satisfactory of college students in information the fabric provided. The problem at the time of the study was that the teacher's performance assessment did not involve students, both assessments were carried out manually by filling out the teacher's learning performance satisfaction form. Therefore, the need for a model in the assessment and the results obtained are not subjective. The development in this study applies the topsis model where the results of this model can provide recommendations for school principals in Teacher Performance Assessment. The application of this topsis model provides a solution/policy in teacher assessment by students which is seen from the ranking results in the form of superior, very good, good and unsatisfactory in the success of providing material. The purpose of this study is to facilitate, supervise, control and see the assessment of the learning performance of SMK Negeri 1 Lhokseumawe teachers in the analysis of information technology-based teacher assessment with a desktop-based topsis model. The results showed that the first rank with a value of 0.616 lia amalia, the second rank with a value of 0.614 and the third rank with a value of 0.605 on behalf of Nazaruddin. The consequences of this test are obtained from the assessment of the topsis version of the trainer's performance.

Pengembangan Handout Berbasis Konstektual Pada Materi Laju Reaksi Untuk SMA/MA Sirry Alvina; Riska Imanda; Mellyzar Mellyzar; Zahratul Fitri
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 15 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

This research aimed to develop contextual-based handouts on reaction rate material with valid, viable, and interesting responses used as teaching materials in the learning process. The development model used in this study is the Borg and Gall models. The validity of the handout developed is divided into two validity of material and media by 84.1% and 93% respectively. Responses from 6 teachers and responses based on the questionnaire given were 91.17% and 90.77%. Handout developed is applied in the learning process with a total of 20 learners using pretest and posttest. Data obtained by researchers using the N-gain formula to find out the improvement of cognitive learning after giving treatment with the number of N-gain scores is 0.513. Based on the hypothesis test with the t-test on the known output sig (2-tailed) = 0.000 < 0.005 then Ho was rejected, meaning there was a significant difference in learning outcomes before and after using contextually based handouts. Based on the data above it was concluded that the results of contextual-based handout development research can be declared valid, feasible, and the response is interesting and can improve the cognitive learning

Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.

Real-time Responses System Dalam Peningkatan Pelayanan Rumah Sakit Arun Lhokseumawe Menggunakan Metode Natural Language Processing: Studi Kasus pada Rumah Sakit Arun untuk Meningkatkan Kecepatan Pelayanan Silfa Maharani Br Padang; Muhammad Fikry; Zahratul Fitri
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8359

Abstract Based on information from the Central Statistics Agency, the population of of Lhokseumawe City reached 191,396 people in 2022, which requires Rumah Sakit Arun Lhokseumawe to enhance its healthcare services to the community. The increase in population presents challenges for the hospital in addressing patients with health issues. Therefore, the author proposes the development of a "Real-Time Responses" system to improve services at Rumah Sakit Arun by utilizing Natural Language Processing (NLP) methods. This system aims to assist patients and the public in seeking information related to health and services available at the hospital. The approach used in developing the chatbot Long Short Term Memory (LSTM) is a component of NLP technology. NLP, in turn, is a field of artificial intelligence (AI) that allows computers to comprehend human text and speech. This technology combines linguistic computation with statistical models, machine learning, and deep learning, allowing computers to process human language in text or voice form and fully comprehend the meaning and sentiment of the writer or speaker. By applying this technology, it becomes expected that communication between the hospital and the community will become more effective, and access to health information can be obtained more quickly, supporting the enhancement of healthcare service quality in Lhokseumawe. Keywords: chatbot, NLP, LSTM, Arun Lhokseumawe, information

Optimalisasi Blue Economy Berbasis IoT dalam Pengawasan Kualitas Air Tambak untuk Sustainability UMKM di Kabupaten Aceh Utara Nunsina; Eva Darnila; Munawwar Khalil; Mustaqim; Zahratul Fitri
Jurnal Malikussaleh Mengabdi Vol. 4 No. 2 (2025): Jurnal Malikussaleh Mengabdi, Oktober 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i02.24783

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan produktivitas dan keberlanjutan UMKM sektor perikanan di Kabupaten Aceh Utara melalui penerapan konsep Blue Economy berbasis teknologi Internet of Things (IoT) untuk pengawasan kualitas air tambak. Permasalahan utama yang dihadapi mitra adalah tidak tersedianya sarana untuk memantau kualitas air secara real-time sehingga berpotensi menurunkan hasil produksi. Kegiatan dilakukan melalui sosialisasi, pelatihan, instalasi perangkat IoT, kalibrasi sensor, serta pendampingan penggunaan sistem. Hasil pelaksanaan menunjukkan bahwa penggunaan IoT mampu memberikan data kualitas air secara akurat dan cepat, sehingga petambak dapat mengambil langkah preventif maupun korektif tepat waktu. Hal ini berdampak pada peningkatan hasil panen, efisiensi penggunaan pakan dan air, serta pengurangan risiko kerugian. Program pengabdian kepada masyarakat ini mendukung tercapainya prinsip Blue Economy dan Sustainable Development Goals (SDGs) di sektor perikanan budidaya udang vaname di Aceh Utara.

Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.

Implementation of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) Algorithm for Rice Price Prediction Ezra Sasqia Syahna; Zara Yunizar; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstrak Studi ini mengimplementasikan model Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) untuk memprediksi harga beras ( gabah ) berdasarkan data historis dari tahun 2020 hingga 2024. Dengan memanfaatkan data yang diperoleh dari Investing.com, penelitian ini mengintegrasikan variabel eksternal utama seperti suhu, harga pupuk, dan tingkat produksi untuk meningkatkan akurasi prediksi. Metodologi ini terdiri dari langkah-langkah sistematis, termasuk pengumpulan data, pemrosesan, dan evaluasi model, dengan menggunakan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE) untuk menilai kinerja. Temuan tersebut mengungkapkan korelasi yang kuat antara harga pasar yang diprediksi dan aktual, khususnya dalam kategori harga penutupan, yang mencapai MAPE sebesar 1,354%. Metrik evaluasi selanjutnya mengonfirmasi kekokohan model, dengan harga penutupan menunjukkan MSE terendah sebesar 299.629,64 dan RMSE sebesar 547,38. Meskipun kategori harga tertinggi menunjukkan MAPE yang sedikit lebih tinggi, yaitu 2,007%, semua kategori tetap berada di bawah ambang batas yang dapat diterima, yaitu 2%, yang menunjukkan akurasi prediksi yang memuaskan. Sebagai kesimpulan, model SARIMAX menunjukkan efektivitas yang signifikan dalam peramalan harga beras, yang memberikan wawasan berharga bagi para pemangku kepentingan di pasar pertanian. Implementasi dalam aplikasi web memfasilitasi prediksi secara real-time, yang mendukung pengambilan keputusan yang tepat, dan meningkatkan strategi pasar. Kata kunci : SARIMAX; harga beras; model prediksi; MAPE; pasar pertanian; analisis deret waktu.

Implementation Of The Adaboost Method On Linear Kernel Svm For Classifying Pip Assistance Recipients At SMP Negeri 2 Kejuruan Muda Muhammad Fahri Al Fikri; Asrianda Asrianda; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract: This study examines the application of the AdaBoost algorithm to a Linear Kernel Support Vector Machine (SVM) for determining student eligibility for the Indonesian Smart Program (PIP) at SMP N 2 Kejuruan Muda. The main objective is to improve the accuracy and fairness of the PIP aid distribution using advanced machine learning techniques. The dataset used comprises 500 student records, which include demographic, academic, and economic factors. The dataset was divided into training and testing sets, with the AdaBoost algorithm applied to enhance the SVM model’s performance. The study found that the SVM model optimized with AdaBoost was able to classify 91 students as eligible for PIP aid, achieving an impressive accuracy rate of 97.85%. Only 2 students were classified as ineligible, representing 2.15% of the total sample. When compared to the standard SVM model, which also classified 91 students as eligible, the key advantage of AdaBoost lies in its ability to handle borderline data more effectively. AdaBoost improves the classification of students whose eligibility was less clear by reinforcing the importance of difficult-to-classify instances. The model’s higher precision on edge cases indicates that AdaBoost offers a significant improvement over traditional SVM models in handling complex classification tasks. This research concludes that incorporating AdaBoost into SVM models provides a more robust and accurate method for determining student eligibility for government aid programs such as PIP. Keywords: AdaBoost, SVM, Indonesian Smart Program, PIP aid, machine learning, student eligibility, classification.

Algorithm Implementation C4.5 For Classification Food Menu to Prevent Stunting in Children Rizki Fadhilah Ramadhani; Bustami; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Stunting in childhood is one of the most significant obstacles to human development and globally affects about 162 million children under five. One effort to prevent stunting is a program to increase the nutritional intake of the community, especially children under five, by providing supplementary food (PMT). Classification is one of the data processing techniques that can be used in this process. The results obtained from the study show that the designed system can input training data and data for classification so that the health centre and guardians can determine the good and bad food menus according to the existing data of toddlers. Based on the results of testing with training data and testing data with a ratio of 80:20 from a dataset of 200 data, namely 160 training data, and 40 test data using the C4.5 algorithm obtained in dataset 1 obtained an accuracy value of 82,5%, precision value of 0.96, recall value of 0,8 and F1-score of 0,87273, then in dataset 2 obtained an accuracy value of 72,5%, precision value 0,75, recall value 0,84 and F1-score value 0,79245.

Implementasi Metode Fisher-Yates Shuffle Dan Metode Finite State Machine Pada Game Edukasi Untuk Meningkatkan Minat Belajar Siswa Anak Sekolah Dasar Rizal, Muhammad; Rozzi Kesuma Dinata; Zahratul Fitri
Jurnal Elektronika dan Teknologi Informasi Vol 7 No 1 (2026): Maret 2026
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v7i1.582

The use of educational games as interactive learning media is one solution to increase learning motivation and understanding among elementary school students. This study aims to implement the Fisher–Yates Shuffle (FYS) and Finite State Machine (FSM) methods in the development of a Unity-based educational game for Natural Sciences (IPA) and Social Sciences (IPS), and to evaluate its effectiveness in improving students’ learning outcomes. The system was developed using the Multimedia Development Life Cycle (MDLC), which consists of concept, design, assembly, testing, and distribution stages. FYS was applied to randomize quiz questions and answer options, while FSM was used to manage game flow and scene transitions in a structured manner. System testing was conducted using black-box testing, and learning effectiveness was evaluated through pre-test and post-test involving grade III and IV elementary school students. The results indicate an increase in students’ average scores after using the educational game, with improvement percentages ranging from 22% to 25%. In addition, teacher questionnaire results show that the game is feasible, easy to use, and beneficial as a supporting learning medium. Therefore, the developed Unity-based educational game is effective in enhancing students’ understanding of IPA and IPS subjects

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