Articles
<![CDATA[Penerapan Algoritma C4.5 Berbasis Particle Swarm Optimization (PSO) Untuk Deteksi Kanker Payudara ]]>
<![CDATA[Haris Luthfi, Muhammad]]>;
<![CDATA[Chairani, Chairani]]>
<![CDATA[ JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER) ]]>
Publisher : <![CDATA[Teknik Komputer Politeknik Negeri Sriwijaya ]]>
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DOI: 10.5281/zenodo.13293601
<![CDATA[This research is motivated by the challenges associated with low accuracy and effectiveness in classification models when dealing with complex data. To address these challenges, the study aims to assess and improve the performance of classification models by integrating Particle Swarm Optimization (PSO) with Decision Tree C4.5 in RapidMiner. The approach involves conducting experiments where PSO is applied to optimize the parameters of Decision Tree C4.5, followed by evaluating the performance of the resulting model. The experimental results show a significant improvement, with model accuracy reaching 99.34%, precision up to 99.65%, recall at 99.30%, and Area Under the Curve (AUC) at 0.997. These findings demonstrate that the combination of PSO and Decision Tree C4.5 can significantly enhance classification effectiveness, making it a viable method for data processing applications requiring high accuracy. Â Keywords:Particle Swarm Optimization (PSO), Decision Tree C4.5, RapidMiner, accuracy, precision, recall.]]>
<![CDATA[Kombinasi Algoritma TF-IDF dan Fuzzy Matching untuk Deteksi Kemiripan Judul Skripsi ]]>
<![CDATA[Azima, Muhammad Fauzan]]>;
<![CDATA[Nur Listanto, Arif]]>;
<![CDATA[Fitria, Fitria]]>;
<![CDATA[Chairani, Chairani]]>
<![CDATA[ TEKNIKA Vol. 19 No. 1 (2025): Teknika Januari 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Sriwijaya ]]>
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DOI: 10.5281/zenodo.13731519
<![CDATA[Dalam era informasi digital, deteksi kemiripan teks menjadi penting untuk berbagai aplikasi seperti plagiarisme, pengelompokan dokumen, dan penyaringan informasi. Penelitian ini bertujuan untuk mengembangkan metode yang efektif dalam mendeteksi kemiripan teks dengan menggabungkan algoritma TF-IDF dan Fuzzy Matching. Alasan pemilihan topik ini didasarkan pada kebutuhan akan akurasi yang lebih tinggi dalam mengidentifikasi kemiripan teks yang seringkali tidak dapat diatasi dengan metode konvensional secara memadai. Metode penelitian ini melibatkan penggunaan TF-IDF untuk mengekstraksi fitur penting dari teks, yang kemudian dipadukan dengan Fuzzy Matching untuk mengatasi variasi dan ketidakpastian dalam teks yang dibandingkan. Hasil penelitian menunjukkan bahwa kombinasi kedua algoritma ini mampu meningkatkan akurasi deteksi kemiripan teks dibandingkan dengan penggunaan salah satu algoritma secara terpisah. Pengujian dilakukan pada dataset Judul Skripsi Prodi Teknik Informatika IIB Darmajaya dengan variasi teks, dan hasilnya menunjukkan peningkatan yang signifikan dalam tingkat keakuratan yang tinggi. Evaluasi model menggunakan precision menunjukan akurasi sebesar 88,89%. Kesimpulan penelitian ini menegaskan pentingnya pendekatan gabungan TF-IDF dan Fuzzy Matching dalam aplikasi deteksi kemiripan teks, yang dapat memberikan kontribusi signifikan terhadap peningkatan kualitas dan efisiensi pengelolaan informasi digital.]]>
<![CDATA[Penerapan Algoritma K-Means Clustering Dalam Mendeteksi Kerusakan Perangkat Laboratorium Komputer Berbasis Android ]]>
<![CDATA[Pratama, Febri]]>;
<![CDATA[Chairani, Chairani]]>;
<![CDATA[Azima, Muhammad Fauzan]]>
<![CDATA[ TEKNIKA Vol. 19 No. 1 (2025): Teknika Januari 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Sriwijaya ]]>
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DOI: 10.5281/zenodo.14043075
<![CDATA[Permasalahan utama dalam penelitian ini adalah terbatasnya sistem pencatatan dan monitoring kondisi laboratorium komputer di Institut Informatika dan Bisnis Darmajaya, yang menyulitkan asisten laboratorium dalam pencatatan status komputer dan mengelompokkan hasil monitoring. Penelitian ini bertujuan untuk mengembangkan sistem informasi berbasis Android yang memudahkan proses pengecekan dan pemantauan kondisi komputer secara real-time, serta mendukung kepala laboratorium dalam pengambilan keputusan berdasarkan kinerja asisten laboratorium per semester. Metode yang digunakan dalam pengembangan sistem ini adalah Extreme Programming untuk memastikan fleksibilitas dan peningkatan pengalaman pengguna. Algoritma K-Means Clustering diterapkan untuk mengelompokkan data kerusakan perangkat laboratorium berdasarkan tingkat kerusakan, sehingga membantu dalam prioritas pemeliharaan. Hasil penelitian menunjukkan bahwa aplikasi monitoring berbasis Android ini dapat secara efektif mengelompokkan dan menampilkan status kerusakan perangkat, serta memberikan notifikasi kerusakan secara real-time. Sistem ini meningkatkan efisiensi monitoring laboratorium, mempermudah pengelolaan, dan membantu pemangku kepentingan dalam proses perbaikan dan pemeliharaan perangkat laboratorium.]]>
<![CDATA[Analysis of Class VII Students' Learning Motivation at SMPN 13 Bontoa, Maros Regency in Differentiated Social Studies Learning ]]>
<![CDATA[Chairani, Chairani]]>;
<![CDATA[Elpisah, Elpisah]]>;
<![CDATA[W, Muhammad Fahreza]]>
<![CDATA[ AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Vol 4, No 1 (2025): January 2025 ]]>
Publisher : <![CDATA[CV. Rayyan Dwi Bharata ]]>
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DOI: 10.57235/aurelia.v4i1.3534
<![CDATA[This research aims to determine and describe the motivation of class VII students in implementing differentiated social studies learning at SMPN 13 Bontoa, Maros Regency. This type of research is descriptive research with a qualitative approach. The data source used is the primary data source. The subjects of this research were 3 class VII students at SMPN 13 Bontoa, Maros Regency with the criteria for high, medium and low learning motivation in differentiated social studies learning. Data collection techniques using questionnaires, observation and interviews. The data analysis technique used is the Miles and Huberman model. The results of this research show that students with high learning motivation criteria have high differentiated social studies learning motivation, this is seen based on the five motivation indicators, of which four meet the learning motivation variable. Then, based on the five indicators studied, students with medium and low learning motivation criteria only met one learning motivation variable. However, students with medium learning motivation criteria have higher differentiated social studies learning motivation than students with low criteria. This can be seen based on the items that medium criteria students do not always fulfill, namely 5 items, while low criteria students do not always fulfill 8 items out of a total of 13 items studied.]]>
<![CDATA[COMPARISON OF TREE IMPLEMENTATION, REGRESSION LOGISTICS, AND RANDOM FOREST TO DETECT IRIS TYPES ]]>
<![CDATA[Siti Mukodimah]]>;
<![CDATA[Chairani Fauzi]]>
<![CDATA[ Jurnal TAM (Technology Acceptance Model) Vol 12, No 2 (2021): Jurnal TAM (Technology Acceptance Model) ]]>
Publisher : <![CDATA[LPPM STMIK Pringsewu ]]>
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DOI: 10.56327/jurnaltam.v12i2.1074
<![CDATA[Iris is a genus of 260-300 species of flowering plants with striking flower colors and has a dominant color in each region. The name iris is taken from the Greek word for rainbow, which is also the name for the Greek goddess of the rainbow, Iris. The number of types of iris plants with almost the same physical characteristics, especially in the pistil and crown, causes the misdetection of iris plant types. Iris plants are deliberately used because data is already available digitally on the internet and software such as orange and is widely used as a material for classifying objects. This research was conducted to classify iris plant types using three algorithms, namely Tree algorithm, Regression Logistics, and Random Forest. Classification algorithms are a learning method for predicting the value of a group of attributes in describing and distinguishing a class of data or concepts that aim to predict a class of objects whose class labels are unknown. The results showed the largest AUC (Area Under Curve) value obtained by the Random Forest method. AUC accuracy is said to be perfect when the AUC value reaches 1,000 and the accuracy is poor if the AUC value is below 0.500. As for the precision value of the three models used Random Forest has the highest precision value. From the data tests that have been done training and testing can be seen that the level of accuracy of testing of the three models where the Random Forest model is superior as a method for classification of irises.]]>
<![CDATA[Utilization of Satellite Imagery and GIS for Mapping Potential Anchovy Fishing Areas in East Lampung ]]>
<![CDATA[Rifki, Rifki Arif]]>;
<![CDATA[Chairani, Chairani]]>;
<![CDATA[Sriyanto, Sriyanto]]>
<![CDATA[ INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July ]]>
Publisher : <![CDATA[P3M Politeknik Negeri Bengkalis ]]>
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DOI: 10.35314/t4fqfq25
<![CDATA[This study utilises Aqua MODIS satellite imagery from January to December 2023 to analyse Sea Surface Temperature (SST) and chlorophyll-a as primary indicators in mapping Potential Fishing Zones (PFZ) for anchovy in East Lampung. Images were filtered based on minimal cloud cover and seasonal completeness using Level 3 daily data with 1 km resolution. The spatial analysis was conducted using Geographic Information Systems (GIS) to identify areas with SST between 29–31°C and chlorophyll-a concentrations above 0.2 mg/m³, which are considered optimal for anchovy habitat. The results show dynamic seasonal shifts in fishing zones influenced by oceanographic conditions. Compared to previous studies, this research provides more detailed seasonal maps and incorporates local fishing data to strengthen relevance. Despite limitations in temporal continuity due to cloud coverage, the approach demonstrates potential for efficient and sustainable fisheries management in Lampung.]]>
<![CDATA[Upaya Meningkatkan Kemampuan Mengenal Budaya Melalui Kegiatan Bermain Kreatif pada Anak Kelompok B di RA Al-Islam ]]>
<![CDATA[Chairani, Chairani]]>
<![CDATA[ Jurnal Ilmiah Mahasiswa Pendidikan Agama Islam [JIMPAI] Vol 5, No 2 (2025) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Sumatera Utara ]]>
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<![CDATA[Penelitian ini dilakukan dengan tujuan untuk mendeskripsikan kemampuan mengenal budaya pada anak RA Al-Islam, kegiatan bermain kreatif yang diterapkan pada anak RA Al-Islam, dan peningkatan kemampuan anak mengenal budaya melalui kegiatan bermain kreatif pada anak RA Al-Islam. Penelitian ini dilakukan dengan metode kualitatif melalui pendekatan PTK yang dilaksanakan dalam dua siklus yang dirancang secara sistematis dengan beberapa tahapan yaitu perencaanaan, pelaksanaan, pengamatan, dan refleksi. Subjek penelitian ini adalah anak kelompok B di RA Al-Islam yang berjumlah sebanyak 17 anak. Untuk mendapatkan data penelitian maka digunakan teknik observasi, dokumentasi dan tanya jawab. Berdasarkan hasil penelitian diketahui bahwa kemampuan mengenal budaya pada anak RA Al-Islam sebelum dilakukan tindakan masih sangat rendah sebab anak masih belum mampu menyebutkan nama permainan budaya atau tradisional, anak juga belum mampu membedakan antara permainan budaya dengan permainan modern, bahkan anak cenderung lebih paham dengan permainan modern disbanding dengan permainan budaya. Kegiatan bermain kreatif yang diterapkan pada anak RA Al-Islam dikhususkan pada kegiatan bermain permaian engklek dan ular naga karena dua permainan budaya ini melibatkan semua anak yang dibagi dalam kelompok. Disamping itu, kedua permainan ini merupakan permaian budaya yang mudah diajarkan pada anak sehingga anak paham karena dapat dimainkan dimana saja. Terjadi peningkatan kemampuan anak dalam mengenal budaya melalui kegiatan bermain kreatif pada anak RA Al-Islam. Hal ini dibuktikan dengan kemampuan anak secara klasikal yang meningkat pada tiap siklusnya dimana pada kondisi sebelum dilakukan tindakan atau prasiklus kemampuan anak mengenal budaya hanya sebesar 27,45 % dengan kriteria “kurang”. Setelah dilakukan tindakan pada siklus I maka kemampuan mengenal budaya pada anak meningkat menjadi 62,74 % dengan kriteria “baik”, dan pada tindakan siklus II meningkat lebih baik sebesar 84,31 % dengan kriteria “baik sekali”.]]>
<![CDATA[Perbandingan Algoritma SVM dan CNN menggunakan PCA untuk Klasifikasi Kematangan Jeruk Keprok ]]>
<![CDATA[Sunarso, Sunarso]]>;
<![CDATA[Chairani, Chairani]]>;
<![CDATA[Triloka, Joko]]>;
<![CDATA[Kurniawan, Rio]]>
<![CDATA[ Jurnal Ilmu Siber dan Teknologi Digital Vol. 3 No. 2 (2025): Mei ]]>
Publisher : <![CDATA[Penerbit Goodwood ]]>
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DOI: 10.35912/jisted.v3i2.5034
<![CDATA[Purpose: This study aims to compare the SVM and CNN machine learning algorithms by combining PCA as data reduction to see which level of accuracy is higher with orange objects. Methodology/approach: created using the waterfall model, the system used to create the model is matlab ver r2022a, using the help of the python programming language to separate the datasets used, the datasets used come from kaagle including the following (https://www.kaggle.com/datasets/raghavrpotdar/fresh-and-stale-images-of-fruits-and-vegetables), and Orange disease dataset(https://www.kaggle.com/datasets/jonathansilva2020/orange-diseases-dataset). Results/findings: The results obtained from the Matlab test using the CNN and PCA algorithms obtained an accuracy of 76.4% and the SVM and PCA classification models obtained an accuracy of 98.89%. Conclusions: This research was successful with the results of combining the SVM and PCA algorithms which had high accuracy results compared to CNN and PCA. Limitations: In this study, the focus is only on comparing the SVM and CNN algorithms with the help of PCA to see which one has the higher level of accuracy between the two. The dataset was only taken from Kaagle, and the software used to create the model was Matlab. Contribution: This research is expected to be a reference for creating models in the future that can be applied to the classification process of automated products.]]>
<![CDATA[The Role Of Islamic Religious Education In The Formation Of Adolescent Character ]]>
<![CDATA[Nurhaliza, Siti]]>;
<![CDATA[Chairani, Chairani]]>;
<![CDATA[Aditya, Fiqri]]>
<![CDATA[ Educate: Jurnal Ilmu Pendidikan dan Pengajaran Vol 3, No 2 (2024) ]]>
Publisher : <![CDATA[Educate: Jurnal Ilmu Pendidikan dan Pengajaran ]]>
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DOI: 10.56114/edu.v3i2.11527
<![CDATA[This article will discuss the role of Islamic religious education in schools in forming the character of teenagers. Islamic Religious Education (PAI) is one of the most important pillars of character education. Character education will grow well if it starts from instilling a religious spirit in children, therefore PAI material in schools is one of the supports for character education. Through PAI learning, students are taught aqidah as the basis of their religion, taught the Koran and hadith as a guide to life, taught fiqh as legal guidelines in worship, taught Islamic history as a living example, and taught morals as a guide to human behavior whether in the category of good or bad. bad. Therefore, the main goal of PAI learning is the formation of personality in students which is reflected in their behavior and thought patterns in everyday life. Apart from that, the success of PAI learning at school is also determined by the application of appropriate learning methods.]]>
<![CDATA[Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner ]]>
<![CDATA[Tupari, Tupari]]>;
<![CDATA[Abdullah, Syaukani]]>;
<![CDATA[Chairani, Chairani]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023) ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v8i3.5641
<![CDATA[The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively]]>
