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Arifin, Oki
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Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.
Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28354

Abstract

Purpose: This study aims to developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC), which expected will give information and can use by the farmer and industrial food crops. Methods: On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. Result: The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438. Novelty: Prediction system of food crops by data mining.
Evaluasi Kinerja Algoritma Naïve Sylvia, Sylvia; Purnomo, Hendri; Arifin, Oki; Arpan, Atika; Permata, Rizka; Handoko, Dwi; Fitriyah, Fitriyah
Jusikom : Jurnal Sistem Komputer Musirawas Vol 9 No 2 (2024): Jusikom : Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i2.2447

Abstract

Social media sentiment analysis has become increasingly important with the rise of platforms like Twitter and Facebook as sources of public opinion. This study evaluates the performance of three machine learning algorithms—Naïve Bayes, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM)—in classifying sentiment from social media data. Using a dataset in Indonesian, we apply cross-validation techniques to measure accuracy, precision, recall, F1-score, and computation time for each algorithm. The results show that SVM achieves the highest accuracy and F1-score, while Naïve Bayes offers better computational speed. KNN demonstrates the lowest performance in terms of accuracy and efficiency. These findings provide guidance for practitioners and researchers in selecting the appropriate algorithm for sentiment analysis based on their specific needs.
Classification of Orchid Types using Random Forest Method with HOG Features Arifin, Oki; Widyawati, Dewi Kania; Zuriati, Zuriati; Maulini, Rima; Sahlinal, Dwirgo; Sylvia, Sylvia
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Orchids are one of the Indonesian people's most widely cultivated ornamental plants. Orchids are a family ofplants in the Orchidaceae family that includes more than 700 genera and around 28,000 individual species. In terms ofplant morphology, orchids can be distinguished based on the morphology of flowers, leaves, fruits, stems, and roots.Orchid leaves have their characteristics for each type of orchid, such as long, round, or lanceolate. All orchids have leafveins that are parallel to the leaves. This makes it difficult to identify the type of orchid flower, especially for laypeopl ewho are new to orchid cultivation and do not yet know the characteristics of various kinds of orchids. The individualshapes of orchid leaves can be classified using Random Forest and Histogram of Oriented Gradients (HOG). In thisstudy, three types of orchids that are currently popular with orchid lovers were used, namely Cattleya, Phalaenopsis, andVanda orchids taken from public data. The accuracy of this method in classifying orchid types based on leaf morphologycan be measured using a confusion matrix that measures accuracy, precision, recall, and F1-score. The test results showthat this method successfully achieved an accuracy of 98%, with an average precision, recall, and F1-score of 0.98 each.These findings indicate that the model built can classify orchid species with a high level of accuracy based on leafmorphology.
Classification Of Nutrient Deficiency In Lettuce Plants (Lactuca Sativa ) Using Machine Learning Algorithm Zuriati , Zuriati; Widyawati, Dewi Kania; Saputra, Kurniawan; Arifin, Oki
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Plants require appropriate nutrients or nutrients for their growth and development. Inappropriate nutrient levelscan interfere with the plant growth process, resulting in less-than-optimal harvest results. Therefore, it is very importantfor farmers to know the nutrient levels of their plants, neither excessive nor lacking. Identification of nutrient deficienciesin plants such as Lettuce (Lactuca Sativa) traditionally requires careful observation of the physical characteristics of theplant, which is often long-drawn out and stand in need of a high level of accuracy. Leaf color is often used as an indication,for example if it is pale or yellow it can indicate a lack of nitrogen or iron. This requires expertise and experience incultivation for lettuce cultivators. So, a tool is needed that can identify nutrient deficiencies accurately, quickly, and easily.This study aims to overcome this challenge, namely identifying nutrient deficiencies in lettuce plants. This approach utilizesmachine learning technology to distinguish four main classes of deficiencies, namely: nitrogen (N), phosphorus (P), andpotassium (K), as well as normal or healthy lettuce leaf conditions. The proposed research method consists of the followingstages: 1). Lettuce leaf image dataset collection, 2). Preprocessing dataset, 3). Implementation of machine learning usingthe Support Vector Machine (SVM) algorithm. In the implementation of SVM, experiments were carried out by applyingvarious SVM kernel spesifically: Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid, 4). Evaluation of modelperformance. Model performance was evaluated by measuring its level of accuracy in classifying nutrient deficiencies inLettuce leaf image data. The results of the experiment showed that SVM with the RBF kernel had the best accuracy, namely:92%. The findings of this study provide valuable insights into the effectiveness of machine learning approaches inclassifying nutrient deficiencies in Lettuce plants. This study can help farmers to optimize their crop production moreefficiently and accurately.
PENDAMPINGAN PEMBUATAN MEDIA PEMBELAJARAN INTERAKTIF BERDIFERENSIASI KURIKULUM MERDEKA BERBASIS DIGITAL PADA SMA PGRI KATIBUNG Arifin, Oki; Widyawati, Dewi Kania; Wibowo, Yusep Windhu Ari; Ikhsan, Fathurrahman Kurniawan; Sylvia, Sylvia; Nurkhotimah, Jihan Susan; Romanda, Novandro; Djangkaru, Elliana
Jurnal Pengabdian Nasional Vol 6 No 1 (2025)
Publisher : Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/jpn.v5i2.4231

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) dengan melibatkan mitra yaitu SMA PGRI Katibung yang merupakan salah satu sekolah penggerak jenjang SMA yang berlokasi di Desa Tarahan, Kecamatan Katibung, Kabupaten Lampung Selatan, Lampung. Kegiatan PKM dilatar belakangi oleh hasil studi awal bahwa guru di SMA PGRI Katibung belum semuanya mengetahui konsep pembelajaran terdifernsiasi kurikulum merdeka dan belum pernah mendapatkan pelatihan mengenai media pembelajaran interaktif. Selain itu, guru belum memahami konsep dan implementasi pembelajaran terdiferensiasi, guru masih menggunakan pendekatan, media, dan metode mengajar konvensional tanpa melibatkan atau menggunakan media pembelajaran digital. Kemudian yang terakhir, guru belum pernah mengikuti pelatihan pembuatan dan pengembangan media pembelajaran digital interaktif apalagi berbasis teknologi. Tujuan PKM ini adalah untuk mengembangkan media pembelajaran interaktif yang menarik dan relevan untuk meningkatkan kemampuan literasi siswa. Integrasi teknologi dalam pembelajaran diharapkan dapat menciptakan pengalaman belajar yang lebih menarik dan mendukung peningkatan literasi serta numerasi siswa di SMA PGRI Katibung. Metode Pembelajaran yang digunakan adalah 1) Edukasi dan pelatihan, 2) Diskusi dan ceramah, dan 3) Pendampingan pembuatan media pembelajaran interaktif digital. Kata kunci: media pembelajaran, diferensiasi, kurikulum merdeka
Implementasi Metode Forward Selection pada Algoritma Support Vector Machine (SVM) dan Naive Bayes Classifier Kernel Density (Studi Kasus Klasifikasi Jalur Minat SMA) Sasongko, Theopilus Bayu; Arifin, Oki
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1093.895 KB)

Abstract

Peminatan merupakan kegiatan yang disediakan oleh pihak sekolah yang berguna untuk mengakomodasi pilihan minat, bakat, atau kemampuan peserta didik dengan orientasi pemusatan. Penentuan jalur minat umumnya melibatkan banyak attribute. Klasifikasi maupun prediksi pada data mining menggunakan fitur seleksi sangat penting untuk pemilihan attribute yang tepat, karena berpengaruh pada performansi model, oleh sebab itu perlu metode untuk melakukan seleksi atribut. Penelitian ini membandingkan implementasi metode forward selection pada Algoritma SVM dan Naïve Bayes Kernel Density. Studi kasus yang digunakan adalah jalur minat pada siswa SMA pada dua sekolah yang berbeda. Proses pembentukan model klasifikasi dengan menganalisa perubahan kernel, faktor pinalti (C) SVM, number of kernel Naïve bayes kernel density, dan hasil feature subset forward selection. Digunakan lima buah eksperimen kernel SVM yaitu dot (linear), radial (RBF), polynomial, neural, dan anova. Proses uji coba perubahan parameter menggunakan rentang 0.0-100.0. Hasil dari penelitian ini diantaranya adalah feature subset dataset SMA ABC yang terpilih yaitu nilai IPA, tes akademik, abstrak konseptual, analisa sintesa, dan logika numerik, sedangkan feature subset SMA XYZ yaitu nilai IPA, logika numerik, dan analisa sintesa. Hasil pengujian dataset SMA ABC menggunakan algoritma FS-SVM berbasis kernel anova parameter C=10.0 sebesar 99.29%. Sedangkan hasil pengujian dataset SMA XYZ menggunakan algoritma FS-SVM berbasis kernel anova parameter C=10.0 sebesar 95.17%. AbstractSpecialization is an activity provided by the school that is useful to accommodate the choice of interests, talents, or abilities of students with a concentration of orientation. The determination of interest generally involved many attributes. The classification and prediction on the data mining that use the selection feature is very important for the selection of the right attribute, because it affects the performance of the model, therefore a method is needed to select attributes. This study compares the implementation of the forward selection method in the SVM Algorithm and Naïve Bayes Kernel Density. The case study that is used is the interest of students in high school and compared with two different schools. The process of modelling by studying kernel changes, penalty factors (C) SVM, number of kernel Naïve bayes kernels, and the results of features from subset forward selection. Five SVM kernel experiments ared used, namely dot (linear), radial (RBF), polynomial, neural, and anova. The trial process of changes parameters uses the range 0.0-100.0. The results of this study include features of selected ABC SMA subset datas, which are IPA values, academic tests, conceptual abstracts, synthesis analysis, and numerical logic, while the XYZ SMA subset features are IPA values, numerical logic, and synthesis analysis. The test results of the ABC High School dataset that use the kernel-based FS-SVM algorithm parameter C = 10.0 is 99.29%. While the results of testing the XYZ SMA dataset that use the kernel-based FS-SVM algorithm parameter C = 10.0 for 95.17%.
PENINGKATAN KOMPETENSI DIGITAL GURU MELALUI PELATIHAN KODING DAN KECERDASAN ARTIFISIAL BERBASIS DEEP LEARNING DI SMAN 2 KALIANDA Kania Widyawati, Dewi; Arifin, Oki; Maulini, Rima; Zuriati, Zuriati; Sahlinal, Dwirgo; Pratama, Yoga; Ari Wijaya Saputra, I Komang; Bulan Nayla, Amanda
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 11 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i11.4100-4108

Abstract

Implementasi Kurikulum Merdeka di SMAN 2 Kalianda memerlukan pendekatan inovatif seperti pembelajaran mendalam (Deep Learning) yang berfokus pada tiga pilar yaitu pembelajaran sadar (Mindful Learning) menyesuaikan materi dengan kebutuhan siswa dan mendorong fokus penuh,  pembelajaran bermakna (Meaningful Learning) melatih berpikir kritis dan mengaitkan konsep dengan kehidupan nyata, serta pembelajaran menyenangkan (Joyful Learning) menciptakan pengalaman belajar yang interaktif dan memotivasi. Namun, pengintegrasian teknologi seperti pemrograman Python dan kecerdasan artifisial masih terhambat oleh keterbatasan kompetensi guru. Program pengabdian ini bertujuan untuk melatih guru dalam penguasaan koding, kecerdasan artifisial, membimbing pendidik merancang modul berbasis proyek, serta mengembangkan bahan ajar digital yang sesuai dengan Kurikulum Merdeka. Pengabdian ini dilaksanakan melalui pendekatan partisipatif kolaboratif yang melibatkan mitra secara aktif. Metode pelaksanaan melalui lima tahapan yaitu sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan program. Peserta dilatih untuk memahami algoritma pemrograman, pemrograman Python, pembuatan model AI sederhana, dan bahan ajar digital melalui Learning Management System (LMS) sekolah. Pelatihan dan evaluasi berbasis pre-test dan post-test menunjukkan hasil peningkatan signifikan dalam pemahaman peserta dengan rata-rata nilai sebesar 64,88 menjadi 96,63 dan N-gain score sebesar 91,36%. Hal ini menunjukkan efektivitas program dalam meningkatkan pengetahuan peserta tentang koding dan kecerdasan artifisial.
Peningkatan Daya Saing Produk Kelompok Wanita Tani Tepian Melalui Pemasaran Digital dan Inovasi Kemasan Arifin, Oki; Widyawati, Dewi Kania; Desfaryani, Rini; Billah, Muhammad Fahry Arief; Loriko, Hendra Agus; Sari, Chika Imelda
Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 3 (2025)
Publisher : FKIP Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36709/amalilmiah.v6i3.562

Abstract

Suak merupakan salah satu desa di Kabupaten Lampung Selatan memiliki KWT Tepian yang mengolah tepung pisang sebagai produk unggulan. Namun, pemasaran produk masih dilakukan secara konvensional dengan kemasan yang kurang menarik dan terbatas pada pasar lokal. Program pengabdian ini bertujuan untuk meningkatkan daya saing produk melalui pelatihan pemasaran digital dan inovasi kemasan produk. Metode yang diterapkan terdiri dari lima tahapan utama, yaitu sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan program. Peserta dilatih untuk memanfaatkan website e-commerce, media sosial, dan content creation untuk memperluas jangkauan pasar. Selanjutnya, pelatihan desain kemasan agar menarik minat konsumen. Pelatihan dan evaluasi berbasis pre-test dan post-test. Hasil menunjukkan peningkatan rata-rata nilai pengetahuan peserta dari 58.13 menjadi 95.74, dengan n-gain score sebesar 90.18%, sehingga program ini efektif dalam meningkatkan pemahaman peserta. Mitra mampu meningkatkan daya tarik kemasan produk tepung pisang dan memanfaatkan media digital secara efektif untuk memperluas pasar dan meningkatkan penjualan. Program ini memberikan kontribusi nyata dalam mendorong terbukanya akses pasar yang lebih luas dan memperkuat kemandirian ekonomi KWT Tepian di desa Suak.
Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce Zuriati, Zuriati; Widyawati, Dewi Kania; Arifin, Oki; Saputra, Kurniawan; Sriyanto, Sriyanto; Ahmad, Asmala
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7143

Abstract

Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder generalization. This study introduces a hybrid machine learning approach integrating SMOTE, Optuna, and SVM to enhance the accuracy of nutrient deficiency classification using digital leaf image analysis. The dataset, obtained from Kaggle, includes four categories: Nitrogen Deficiency (-N), Phosphorus Deficiency (-P), Potassium Deficiency (-K), and Fully Nutritional (FN). Image features were extracted using MobileNetV2 pretrained on ImageNet and classified with a Support Vector Machine. Three scenarios were tested: (1) SVM before SMOTE, (2) SVM after SMOTE, and (3) Optuna-SVM after SMOTE, evaluated using accuracy, precision, recall, and f1-score. The hybrid model achieved the best performance with accuracy 0.929, precision 0.946, recall 0.835, and f1-score 0.869, outperforming the other scenarios. This hybrid framework effectively addressed class imbalance and improved classification margin stability through adaptive hyperparameter tuning using the Tree Structured Parzen Estimator within Optuna. The novelty of this study lies in combining MobileNetV2 based feature extraction with SMOTE and Optuna-SVM for small agricultural datasets. The proposed approach offers an efficient, accurate, and practical solution for automated nutrient deficiency diagnosis and contributes to the development of AI-driven smart agriculture systems.
Co-Authors Ahmad, Asmala Ari Wijaya Saputra, I Komang Arpan, Atika Billah, Muhammad Fahry Arief Bulan Nayla, Amanda Desfaryani, Rini Djangkaru, Elliana Dwi Handoko Dwirgo Sahlinal, Dwirgo Fathurrahman Kurniawan Ikhsan Fitriyah Fitriyah Halim Fathoni, Halim Hendri Purnomo Kania Widyawati, Dewi Kurniawan Saputra Loriko, Hendra Agus Maulini, Rima Nurkhotimah, Jihan Susan Permata, Rizka Rima Maulini Romanda, Novandro Sari, Chika Imelda Sasongko, Theopilus Bayu Sriyanto, Sriyanto Sylvia, Sylvia Wibowo, Yusep Windhu Ari Widyawati, Dewi Kania Yoga Pratama Zuriati, Zuriati