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All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Biogenerasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Multidisciplinary Applied Natural Science Bulletin of Information Technology (BIT) EXPLORER Jurnal Pengabdian Masyarakat Gemilang (JPMG) Journal of Scientific Research, Education, and Technology Buletin Ilmiah Informatika Teknologi Prosiding Seminar Nasional Unimus International Conferences On Early Childhood Education Proceedings
Andi Ernawati
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Komparasi Kinerja Algoritma Random Forest dan C4.5 untuk Klasifikasi Harga Mobil Ernawati, Andi; Karim, Abdul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 1: September 2024
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i1.95

Abstract

Determining car prices is a crucial aspect of the automotive industry that requires accurate data analysis for strategic decision-making. This study aims to compare the performance of the Random Forest and C4.5 algorithms in classifying car prices based on specific features, such as technical specifications, production year, and market conditions. The dataset used in this study consists of [mention the size and source of the dataset if available], analyzed using a cross-validation approach to ensure the accuracy of the results. The performance of both algorithms is evaluated based on several metrics, including accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm consistently outperforms the C4.5 algorithm across most evaluation metrics, achieving an accuracy of [best Random Forest accuracy] compared to [best C4.5 accuracy]. These findings indicate that the Random Forest algorithm is more effective in handling multivariate data complexity and providing more reliable predictions. The conclusions of this study highlight the potential of Random Forest as the primary method for car price classification, especially in scenarios requiring high accuracy levels. This research also contributes to a comparative understanding of decision-tree-based algorithms for applications in the automotive industry and opens opportunities for further research into developing more adaptive and efficient models.
Analisis Perbandingan Algoritma Klasifikasi Data Mining untuk Penentuan Lokasi Perumahan Ernawati, Andi; Iqbal, Muhammad
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6516

Abstract

This study aims to analyze the application of C5.0 and K-Nearest Neighbor (K-NN) algorithms in the classification process for determining the optimal location for housing. The classification process involves several factors such as land price, accessibility, public facilities, crime rate, infrastructure, land availability, and consumer preferences. The research conducted tests on both algorithms to compare their performance in generating accurate predictions. The results show that the C5.0 algorithm outperforms K-NN, achieving an accuracy rate of 100%, compared to K-NN, which achieved an accuracy of 66.67%. This demonstrates that C5.0 is more effective in modeling data and producing more precise classifications. Therefore, it can be concluded that the use of data mining algorithms, particularly C5.0, greatly assists in the classification process for determining housing locations, providing more optimal results compared to K-NN.
Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors Ernawati, Andi; Khairul; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1783

Abstract

This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.
Uncovering Smartphone Brand Strategies through Specification-Based Clustering and Classification Karim, Abdul; Ernawati, Andi
Buletin Ilmiah Informatika Teknologi Vol. 4 No. 1: September 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v4i1.167

Abstract

In an increasingly saturated smartphone market, brand differentiation through technical specifications has become a core strategy for attracting diverse consumer segments. This study proposes a machine learning approach to uncover underlying brand strategies by leveraging smartphone specifications and market pricing across multiple regions. We utilize unsupervised clustering algorithms (K-Means, DBSCAN) to segment devices based on technical features, followed by supervised classification models (Random Forest, XGBoost) to identify and interpret brand-driven design strategies. The dataset comprises smartphones released in 2024–2025, including attributes such as RAM, camera specifications, processor type, battery capacity, and launch prices in Pakistan, India, China, USA, and Dubai. Our findings reveal distinct clusters that align with different pricing tiers and show clear brand positioning patterns. Feature importance analysis using SHAP highlights battery capacity, screen size, and processor type as critical variables influencing brand classification. This study provides valuable insights for both manufacturers and consumers in understanding competitive product strategies within the global smartphone market.
EFEKTIVITAS ANTIBAKTERI EKSTRAK DAUN TEH HIJAU TERHADAP BAKTERI MYCOBACTERIUM TUBERCULOSIS Misnarliah; Ernawati, Andi
Jurnal Biogenerasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1, Agustus 2024 - Februari 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/biogenerasi.v10i1.4431

Abstract

Tuberculosis is still a global health problem. Prolonged treatment of tuberculosis and using several anti-tuberculosis drugs (OAT) can cause side effects, one of which is multidrug resistance. Cases of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium strains continue to increase. . Research and development of active compounds from medicinal plants to achieve more effective tuberculosis treatment is still being promoted. This research aims to determine the potential and effectiveness of green tea plant (Camellia sinensis) leaf extract in inhibiting the growth of Mycobacterium tuberculosis strain H37Rv bacteria using the LJ (Lowenstein-Jensen) method in vitro. The research samples used were green tea plant leaves (Camellia sinensis) obtained from the Malino Village Tea Plantation, Tinggi Moncong Regency, Gowa District, South Sulawesi. The leaf extract was made in 4 types of concentrations, namely 10, 20, 50 and 100 µg/ml, each of which was tested against the clinical isolate of Mycobacterium tuberculosis strain H37Rv using the LJ (Lowenstein-Jensen) method as the standard for tuberculosis examination. Of the four extracts tested in vitro, only extract concentrations of 50 and 100 µg/ml were able to very strongly inhibit and kill the growth of Mycobacterium tuberculosis strain H37Rv (inhibition percentage of 100%), not a single bacterial colony growth was found during the observation period. The percentage of inhibition of green tea plant leaves (Camellia sinensis) is the same as the percentage of inhibition of the drug rifampicin. Thus, the leaves of the green tea plant (Camellia sinensis) with concentrations of 50 and 100 µg/ml have potential antituberculosis activity and are prospective to be developed as antituberculosis from natural ingredients, and also as an additional therapeutic complement for TB.
The Role of Teachers’ Professional Competence in Implementing School Based Management: Study Analisys at Secondary School in Parepare City of South Sulawesi ProvinceIndonesia Lasadike, Syahruddin; Ernawati, Andi; Abdul Rahman, Mohd. Anuar B.; B. Sihes, Ahmad Johari
International Journal of Evaluation and Research in Education (IJERE) Vol 2, No 3: September 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (148.871 KB)

Abstract

The relationship between the role of professional competence of teachers and the implementation of School-based Management (SBM) is investigated in this study. Survey and interviews had been done to 325 respondents consisted of 13 principals, 226 teachers, and 86 school committee members of public secondary schools (SMPN) in Parepare, South Celebes, Indonesia in this mixed-method study. The results showed that the there is a significant relationship between the teachers’ professional competence and the implementation of SBM. In other words, the increase of the teachers’ professional competence is significantly related to the increase of the implementation of SBM. Furthermore, the results also indicated that teachers’ professional competence significantly affect the implementation of SBM. Additionally, the qualitative results from the interviews indicated that teachers’ professional development in the form of performance, commitment, and motivation improvement can be considered as an important strategy to improve the quality of SBM.DOI : http://dx.doi.org/10.11591/ijere.v2i3.2577
Penentuan Bibit Kelapa Sawit Unggul Dengan Metode ARAS Dan TOPSIS Nur Alam, Sitti; Yesputra, Rolly; Zikra Syah, Arridha; Parini; Ernawati, Andi
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i4.2213

Abstract

The Industrial Era 4.0 opens up great opportunities to increase production, efficiency and sustainability of the palm oil industry. The problem faced by farmers is that farmers are often hampered by limited knowledge and lack of guidance in choosing plant seeds. Because seeds are an important factor in supporting satisfactory results. This research was carried out to help farmers who have difficulty in choosing oil palm seeds which could become a problem for farmers in the future. day. This research uses the ARAS and TOPSIS methods to evaluate seeds based on criteria that have been identified and analyzed, to assess 10 types of superior seeds based on 5 criteria: oil potential, pest resistance, seed price, productive planting period, and maintenance costs. It is hoped that this research can help oil palm farmers increase their productivity and profits, as well as support the sustainability of the palm oil industry in the Industry 4.0 era. The ARAS and TOPSIS methods have proven to be effective in helping farmers choose superior oil palm seeds. From the results of research conducted using the ARAS and TOPSIS methods, VIM 1 seeds were recommended as the best choice based on the points obtained.
Peningkatan Pengarahan Beam dan Estimasi Sudut Kedatangan Berbasis CNN untuk Sistem Antena MIMO Cerdas Karim, Abdul; Purnama, Iwan; Ernawati, Andi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2592

Abstract

This study proposes a Convolutional Neural Network (CNN)–based approach to enhance the intelligence of MIMO antenna systems in Internet of Things (IoT) environments, particularly for modeling the relationship between wireless channel characteristics and achievable communication capacity. Modern MIMO systems face complex challenges due to dynamic channel conditions such as noise, path loss, and multipath fading, which significantly affect data transmission quality. In this research, channel-related features are processed through a structured preprocessing stage before being fed into a CNN model to learn nonlinear relationships among channel parameters. The developed model is designed to predict achievable channel capacity accurately as part of an adaptive and intelligent wireless communication framework. Experimental results show that the proposed CNN model achieves a Test Loss of 0.0317 and a Mean Absolute Error (MAE) of 0.1267 on unseen test data. Visualization of actual versus predicted values indicates that the model demonstrates good generalization across most data ranges, although some deviations remain at extremely high capacity values. Compared to conventional approaches, the CNN-based method shows superior capability in capturing complex correlations among MIMO channel parameters. Therefore, this approach contributes to the development of adaptive and efficient intelligent antenna systems, supporting the growing demands of next-generation IoT communication networks.
Phenotypic and Genotypic Characterization of Bacteria from Diabetic Ulcers: Insights into Resistance and Biofilm Formation Ernawati, Andi; Rusmana, Iman; Wahyudi, Aris Tri; Murtini, Sri; Budiarti, Sri
Journal of Multidisciplinary Applied Natural Science Articles in Press
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.348

Abstract

Diabetic ulcers are prone to colonization by multidrug-resistant bacteria, complicating treatment and recovery. Therefore, understanding the microbial profile and resistance mechanisms is essential for effective clinical management. This study isolated and characterized bacteria from diabetic ulcer patients in West Java, Indonesia. Identification was performed using the selective differential medium test and 16S rRNA sequencing. Antibiotic susceptibility was assessed via the disc diffusion method, and biofilm formation was evaluated using crystal violet staining. PCR amplification targeted blaTEM and sul1 resistance genes. A total of 15 bacterial isolates were identified, predominantly Staphylococcus aureus and Pseudomonas aeruginosa. Six isolates exhibited a multiple antibiotic resistance (MAR) index >0.4 and demonstrated biofilm-forming capacity. The PCR result confirmed the presence of blaTEM and sul1 genes in three isolates. The findings highlight the prevalence of multidrug-resistant, biofilm-forming bacteria in diabetic ulcers, underscoring the need for targeted antimicrobial strategies and resistance monitoring.
Co-Authors - Idris Abdul Karim Ahmad Johari B. Sihes Alam, Sitti Nur Aris Tri Wahyudi Arridha Zikra Syah Aulia, Ananda Ayu Ofta Buton Buton Cucum Sumiati Darmeli Nasution Durrotul Muniroh Ahdaniyah Farta wijaya, Rian Idris Idris Iman Rusmana Iwan Purnama Khairul Mia sumiani Madi Misnarliah Mohd. Anuar B. Abdul Rahman Muhammad Arif Mansur Muhammad Iqbal Nurhaliza Sofyan, Siti Ofta Sari, Ayu Oftasari, Ayu Parini Puridawaty , Brigita Rolly Yesputra Salki Sasmita Saputra, Maulian Sari, Ayu Ofta Siregar, Andree Risky Yuliansyah Sitorus, Zulham Sofyan, Siti Nurhaliza Sri Budiarti Poerwanto sri murtini . Sri Wahyuni Sugeng Santoso Susilawati Syahruddin Lasadike Wijaya, Rian Farta Wijaya, Rian Farta Wijaya