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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Dedikasi Pendidikan Celebes Abdimas: Jurnal Pengabdian Kepada Masyarakat Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi jeti Jurnal Abdi Masyarakat Indonesia Mejuajua Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Bulletin of Engineering Science, Technology and Industry INOVTEK Polbeng - Seri Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) ITEJ (Information Technology Engineering Journals)
Zahratul Fitri
FakultasTeknik Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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APLIKASI SISTEM PENDUKUNG KEPUTUSAN UNTUK PEMBANGUNAN PERUMAHAN DENGAN METODE FUZZY TSUKAMOTO Ananda Faridhatul Ulva; Zahratul Fitri
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 2 No. 2 (2018): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2018
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v2i2.1012

Abstract

Pembangunan perumahan beserta sarana dan prasarananya perlu mendapatkanprioritas mengingat tempat tinggal merupakan salah satu kebutuhan dasar (basic needs) (Maslow). Dalam lingkup pembangunan, masyarakat merupakan pelaku utama pembangunan tersebut. Mengarahkan, membimbing, dan menciptakan suasana yang menunjang pembangunan adalah kewajiban pemerintah Pada penelitian ini, penulis akan mensimulasikan bagaimana merancangan dan mengaplikasikan perangkat lunak system pendukung keputusan untuk pembangunan kompleks perumahan dengan algoritma Tsukamoto. Yang memiliki tujuan untuk merancangan perangkat lunak sistem pendukung keputusan untuk pembangunan kompleks perumahan di daerah Aceh dengan algoritma Tsukamoto dengan bahasa pemograman Java. Serta untuk mengaplikasikan metode Tsukamoto kedalam pengambilan keputusan kelyakan dalam pembuatan perumahan.Kata kunci : Sistem Pendukung Keputusan, Fuzzy Tsukamoto, Perumahan
Classification Analysis Model of Construction Materials to Support Decision-Making in Construction Projects Emi Maulani; Syarifah Asria Nanda; Burhanuddin Burhanuddin; Ananda Faridhatul Ulva; zahratul fitri
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.26978

Abstract

This study aims to analyze and compare the performance of Gaussian Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying the feasibility of construction materials to support decision-making in construction projects. A quantitative comparative study design was applied using 127 samples of structural building materials collected from 15 contractor companies in Lhokseumawe City, Indonesia. The dataset consists of five predictor variables: price, compressive strength, water absorption, delivery time, and supplier rating. Data preprocessing included missing value imputation, outlier handling using the interquartile range method, normalization using Min-Max scaling, and class balancing using Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was conducted using accuracy, precision, recall, F1-score, and AUC, while feature importance was analyzed using permutation importance. The results show that the KNN model (k = 5) outperforms Gaussian Naïve Bayes across all evaluation metrics, achieving an accuracy of 92.11% and an AUC of 0.934.
SISTEM MONITORING DAN PENDETEKSI PENCEMARAN UDARA SEKOLAH BERBASIS INTERNET OF THINGS : INTERNET OF THINGS BASED SCHOOL AIR POLLUTION MONITORING AND DETECTION SYSTEM Aryo Wibisono Putra; Zahratul Fitri; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6347

Abstract

SMK Negeri 3 Lhokseumawe is located in a high activity area surrounded by hotels, workshops and residential areas. Based on internal school data, student complaints related to respiratory problems increased significantly from 8% in 2020 to 30% in 2024. This condition shows the urgency of the need for an air quality monitoring system that is adaptive, efficient, and can operate in real-time in the school environment. This research aims to develop an Internet of Things (IoT)-based air pollution monitoring and early detection system with the integration of Takagi Sugeno Kang (TSK) fuzzy method. The system uses an ESP32 microcontroller connected to MQ-135 (CO₂), MQ-7 (CO), GP2Y1010AU0F (PM10), and DHT22 (temperature and humidity) sensors. In contrast to conventional approaches that only read raw data or rely on fixed thresholds, the TSK fuzzy method is able to adaptively process multivariate data and produce more precise air quality classifications. Data is sent in real-time to the server and displayed via website and LCD. Tests were conducted for 7 hours with 100 samples under relatively controlled environmental conditions. The implementation results show that the system runs stably and accurately, one of which is the measurement of CO 8.26 ppm, CO₂ 587 ppm, PM10 36.34 µg/m³, temperature 29.60°C, and humidity 76.50%, which is classified as “Fair” based on a fuzzy value of 2.303735. This research fills the literature gap by optimizing the TSK fuzzy method on a resource-limited device (ESP32), and offers novelty in the presentation of air quality information quickly and contextually to support health risk mitigation in educational environments.
OPTIMASI JUMLAH CLUSTER PADA K-MEANS CLUSTERING MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK PENGELOMPOKAN UKT MAHASISWA Ira Fazira; Zahratul Fitri; Risawandi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6396

Abstract

The determination of the Single Tuition Fee (UKT) group in higher education faces challenges in terms of distribution fairness due to the inappropriate grouping of students' socio-economic conditions. The K-Means algorithm, while effective in handling large-scale data at good computational speeds, has a drawback in determining the optimal number of clusters automatically. This study aims to implement the integration of Particle Swarm Optimization (PSO) with K-Means Clustering in the grouping of student UKT data and evaluate the improvement of the quality  of clustering produced compared to conventional methods. The study uses a dataset of 437 new students of the Faculty of Engineering in 2024 from Malikussaleh University with 8 attributes that describe family socioeconomic conditions. The research stages include pre-processing of data, determination of the optimal number of clusters using PSO, implementation of K-Means clustering with optimal K, model evaluation using Silhouette Coefficient and Davies-Bouldin Index, and model comparison using the elbow method. The results of the study showed that PSO succeeded in determining the optimal number of clusters as many as 3 clusters. The implementation of K-Means with K=3 resulted in the distribution of clusters: cluster 0 (40 students/9.2%), cluster 1 (93 students/21.3%), and cluster 2 (304 students/69.6%). Clustering quality evaluation  resulted in  a Silhouette Coefficient of 0.278062 and  a Davies-Bouldin Index of 1.430505 indicating adequate cluster formation with fairly good internal cohesion and reasonable separation between clusters. Comparison with  the conventional K-Means method  using the Elbow Method shows the advantage of PSO-K-Means with  a higher Silhouette Coefficient (0.278062 vs 0.250300) and  a competitive Davies-Bouldin Index (1.430505 vs 1.315400). This research proves that the combination of PSO and K-Means can provide a more optimal solution in the grouping of student UKT to support a fairer determination of tuition fees based on family economic ability.
PREDIKSI STOK OBAT TB DENGAN ARIMA DAN ANALISIS VOLATILITAS RESIDUAL DI PUSKESMAS BANDA SAKTI Khanifa Muslimah Siregar; Zahratul Fitri; Fajriana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6398

Abstract

Effective drug stock management is essential in healthcare services, particularly for infectious diseases such as pulmonary tuberculosis. This study aims to forecast TB drug stock using the ARIMA model and analyze residual volatility based on data from Banda Sakti Public Health Center, Lhokseumawe City. It focuses on applying predictive modeling at the primary healthcare level, which has rarely been addressed in previous studies. The dataset covers six drug types from January 2021 to December 2024. ARIMA models were selected automatically using Python and evaluated using sMAPE, MAE, and RMSE. Results show that ARIMA was successfully applied to four drug types, with sMAPE ranging from 31% to 41%, which is acceptable for short-term planning. The ARCH test produced p-values > 0.05, indicating that GARCH was not necessary. Two drug types could not be modeled due to zero-constant and sporadic data patterns. The proposed system can assist pharmacy staff in planning procurement and safety stock at the primary care level.
PREDICTION OF SUSTAINABILITY OF FAMILY PLANNING PARTICIPANTS BASED ON DEMOGRAPHIC CHARACTERISTICS USING RANDOM FOREST Dara Fazila; Zahratul Fitri; Lidya Rosnita
Bulletin of Engineering Science, Technology and Industry Vol. 4 No. 3 (2026): September - ON PROGESS
Publisher : PT. Radja Intercontinental Publishing

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

Abstract

Family Planning (KB) is one of the government programs aimed at controlling population growth and improving family welfare. However, some Family Planning participants discontinued the use of contraceptives, which may affect the success of the program. Therefore, this study aims to develop a prediction system for the continuity of Family Planning participants in Cot Girek District using the Random Forest algorithm. The study used 1,000 Family Planning participant records containing demographic, socioeconomic, and contraceptive-related attributes. After the data preprocessing stage, 998 records were used to construct the prediction model. The research involved data preprocessing, Random Forest model construction, and model evaluation using Hold-Out Validation and 5-Fold Cross Validation. The results showed that the Age of the Youngest Child attribute had the highest Gini Gain value of 0.3486, indicating that it was the most influential factor in predicting the continuity of Family Planning participants. The model achieved an Accuracy of 93%, Precision of 93.33%, Recall of 96.18%, and F1-Score of 94.74%, while 5-Fold Cross Validation produced an average accuracy of 97.40% with a standard deviation of ±4.95%. In addition, Black Box Testing confirmed that all system functions are operated according to user requirements. These findings indicate that the Random Forest algorithm can effectively predict the continuity of Family Planning participants and can be used as a decision-support tool to assist Family Planning officers in monitoring and providing more targeted assistance to participants.
Classification of Coronary Heart Disease Based on Community Health Centre Medical Record Data Using SVM Algorithm M Reza Kausar; Wahyu Fuadi; Zahratul Fitri
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ng11kk81

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide and demands a fast and accurate diagnostic system, especially in community health centres (Puskesmas) where medical resources are limited. This study aims to develop a classification system for CHD using the Support Vector Machine (SVM) algorithm based on numerical medical record data. It also addresses the gap in previous studies that rarely applied SVM to tabular data from primary healthcare facilities. The methodology includes variable weighting, min-max normalization, model training with a linear kernel, and performance evaluation using a confusion matrix. The dataset consists of 100 patient records with variables such as age, blood pressure, heart rate, respiratory rate, and chest pain. The results show that the SVM model achieved an accuracy of 95%, a precision of 100%, recall of 88.9%, and an F1-score of 94.1%. The model was further integrated into a web-based application using Flask to support automated early diagnosis. This study demonstrates that SVM is effective in classifying heart disease based on medical records and offers a practical solution to improve healthcare service quality in Puskesmas.
PENGELOMPOKAN UMKM SEKTOR INDUSTRI MIKRO MENGGUNAKAN METODE K-MEANS CLUSTERING BERBASIS ANDROID DI ACEH UTARA Kamilna kamilna; Safwandi Safwandi; Zahratul Fitri
Djtechno: Jurnal Teknologi Informasi Vol 7, No 2 (2026): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i2.9670

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) merupakan salah satu sektor yang berperan penting dalam mendukung perekonomian daerah. Di Kabupaten Aceh Utara, jumlah UMKM mikro sektor industri yang cukup besar menyebabkan proses pengelompokan data secara manual menjadi kurang efektif dalam mendukung penyusunan program pembinaan. Penelitian ini bertujuan mengimplementasikan metode K-Means Clustering ke dalam aplikasi berbasis Android untuk mengelompokkan UMKM mikro sektor industri berdasarkan karakteristik aset dan omset. Data penelitian diperoleh dari Dinas Perdagangan, Perindustrian, Koperasi, dan Usaha Kecil Menengah Kabupaten Aceh Utara sebanyak 1.151 data yang telah melalui proses preprocessing. Pengujian dilakukan dalam dua tahap, yaitu pengujian algoritma menggunakan sampel sebanyak 100 data UMKM serta implementasi pada aplikasi menggunakan seluruh data penelitian. Hasil pengujian terhadap 100 data menghasilkan Cluster 1 sebanyak 71 UMKM, Cluster 2 sebanyak 19 UMKM, dan Cluster 3 sebanyak 10 UMKM. Implementasi pada aplikasi mencapai kondisi konvergen pada iterasi ke-6 dengan hasil akhir Cluster 1 sebanyak 1.106 UMKM (96,09%), Cluster 2 sebanyak 44 UMKM (3,82%), dan Cluster 3 sebanyak 1 UMKM (0,09%). Hasil penelitian menunjukkan bahwa metode K-Means Clustering mampu mengelompokkan UMKM berdasarkan tingkat kemiripan karakteristik aset dan omset serta berhasil diimplementasikan ke dalam aplikasi berbasis Android. Aplikasi yang dikembangkan diharapkan dapat membantu Dinas Perdagangan, Perindustrian, Koperasi, dan UKM Kabupaten Aceh Utara dalam memperoleh informasi pengelompokan UMKM sebagai dasar penyusunan program pembinaan dan pengambilan keputusan berbasis data
Implementasi Metode Fisher-Yates Shuffle Dan Metode Finite State Machine Pada Game Edukasi Untuk Meningkatkan Minat Belajar Siswa Anak Sekolah Dasar Muhammad Rizal; 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

Abstract

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
Penerapan Media Pembelajaran Interaktif Berbasis Blended Untuk Meningkatkan Kualitas Belajar Siswa Di Smkn 3 Lhokseumawe Zahratul Fitri; Muhammad Zulfat Akbar; Mutammimul Ula
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 5 No. 1 (2021): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2021
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v5i1.4857

Abstract

Pendidikan merupakan usaha sadar yang dilakukan peserta didik untuk mengembangkan segala potensi yang dimilikinya, Pendidikan memegang peranan penting untuk kelangsungan kehidupan suatu bangsa dalam meningkatkan dan mengembangkan kualitas sumber daya manusia, Keaktifan siswa dalam proses pembelajaran harus diperhatikan dan dipahami oleh setiap guru, sehingga guru dapat mengetahui sudah sejauh mana pemahaman siswa terhadap proses pembelajaran yang dilakukan oleh guru di kelas.Tujuan penelitian ini adalah untuk menerapkan interaktif learning berbasis pembelajaran blended untuk meningkatkan kualitas belajar siswa yang menitik beratkan pada model pembelajaran projek base learning(PjBL). Metode penelitian yang digunakan penelitian ini yaitu penelitian menggunakan tindakan kelas, penelitian ini dilaksakan dengan 3 unsur yaitu kelas, guru dan siswa yang menjadi titik focus penelitian, penelitian ini bersubjek siswa SMKN 3 Lhokseumawe berjumlah 42 siswa,Tahapan penelitian yaitu Observasi kelas, perencanaan tindakan kelas (tindakan luring dan daring), pengamatan kelas, dan refleksi hasil (Secara Daring). Hasil penelitian menunjukan Pembelajaran menggunakan model Pembelajaran Project Based Learning secara daring dan luring yaitu meningkatnya kualitas belajar siswa pada mata pelajaran simulasi digital dengan materi pengolahan kata, angka dan presentasi efektif, dengan data peningkatan dari sebelum menggunakan model pembelajaran ini yaitu rata kelas 49,52 menjadi 83 setelah menggunakan model pembelajaran Project based learning dengan interaktif learning dan nilai rata-rata kelas dari nilai 59,28 menjadi 86,38 setelahanya. Kata Kunci :Interaktif Learning, Pembelajaran Blended,Project Base Learning(PjBL) Kualitas Siswa
Co-Authors Ade Irfan Ananda Faridhatul Ulva Angga Pratama Aryo Wibisono Putra Burhanuddin Burhanuddin Cut Ita Erliana Dara Fazila Emi Maulani Fajriana Fajriana, Fajriana Fajrul Wahdi Ginting Fakrurrazi Ira Fazira Kamilna kamilna Khanifa Muslimah Siregar Lidya Rosnita M Reza Kausar Mauliza, Ade Muhammad Faisal Muhammad Rahmad Zainal Muhammad Rizal Muhammad Zulfat Akbar Mukti Qamal Mutammimul Ula Mutia Fonna Nurdin Nurdin Putri Rahma Risawandi, Risawandi Rozzi Kesuma Dinata Safriana Safriana Safwandi Safwandi Said Fadlan Anshari Selly Alfika selly Syarifah Zahra Tiara Razaqa Sakinah Wahyu Fuadi