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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Community Development Journal: Jurnal Pengabdian Masyarakat SELODANG MAYANG Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Komputer dan Teknologi (JUKOMTEK) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Innovative and Creativity
Dicky Apdillah
Universitas Asahan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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DETEKSI PENYAKIT DAUN KELAPA SAWIT MENGGUNAKAN DEEP LEARNING BERBASIS CONVOLUTIONAL NEURAL NETWORK (CNN) Seprina Aulia Putri; Dicky Apdillah
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6393

Abstract

Abstract: Oil palm leaf diseases can significantly reduce harvest productivity. Manual identification is considered less effective because it requires a long time and carries the risk of misidentification. This study develops a leaf image detection system using a CNN with the MobileNetV2 architecture, implemented on both web and mobile platforms. A dataset of 1,200 images from Kaggle and field observations covers four classes: leaf spot, anthracnose, yellow stripe, and Other. The research stages include data collection, image preprocessing, dataset splitting, CNN model training using the MobileNetV2 architecture, model testing, performance evaluation, and system implementation on web and mobile platforms. Evaluation was conducted using confusion matrix, accuracy, precision, recall, and F1-score parameters. Based on testing results, the model achieved 93.33% accuracy, 93.71% precision, 92.92% recall, and 93.31% F1-score. The developed system was successfully implemented on web and mobile platforms with features such as image upload, automatic classification, detection history, and disease management recommendations. With this system, users are expected to perform early detection of oil palm leaf diseases more quickly, practically, and accurately. Keywords: CNN, Deep Learning, Image Classification, MobileNetV2, Oil Palm Leaf Disease.   Abstrak: Penyakit daun kelapa sawit dapat menurunkan produktivitas panen secara signifikan. Identifikasi manual dinilai kurang efektif karena membutuhkan waktu lama dan berpotensi salah identifikasi. Penelitian ini membangun sistem deteksi citra daun menggunakan CNN arsitektur MobileNetV2 berbasis web dan mobile. Dataset 1.200 citra dari Kaggle dan observasi lapangan mencakup empat kelas: bercak daun, antraknosa, garis kuning, dan Other. Tahapan penelitian meliputi pengumpulan data, preprocessing citra, pembagian dataset, pelatihan model CNN menggunakan arsitektur MobileNetV2, pengujian model, evaluasi performa, serta implementasi sistem berbasis web dan mobile. Proses evaluasi dilakukan menggunakan parameter confusion matrix, accuracy, precision, recall, dan F1-score. Berdasarkan hasil pengujian, model memperoleh akurasi 93,33%, precision 93,71%, recall 92,92%, dan F1-score 93,31%. Sistem yang dibangun berhasil diimplementasikan pada platform web dan mobile dengan fitur unggah citra, proses klasifikasi otomatis, riwayat deteksi, serta rekomendasi penanganan penyakit. Dengan adanya sistem ini, pengguna diharapkan dapat melakukan deteksi dini penyakit daun kelapa sawit secara lebih cepat, praktis, dan akurat. Kata Kunci: CNN, Deep Learning, Klasifikasi Citra, MobileNetV2, Penyakit Daun Sawit.
KOMPARASI ALGORITMA K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK KLASIFIKASI KELAYAKAN EKSPOR KOPI ARABIKA DENGAN CORRELATION-BASED FEATURE SELECTION Diva Agustin Purba; Nazma Aulia; Pujawati; Dicky Apdillah; Bambang Irwansyah; Harmayani
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6589

Abstract

Abstract: Arabica coffee is a high-value export commodity for the Indonesian economy. To maintain global competitiveness, coffee beans must meet export feasibility quality standards based on the cupping score from the Coffee Quality Institute (CQI). This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Gaussian Naive Bayes algorithms in classifying the export feasibility of Arabica coffee beans. In sensory quality testing, high-dimensional attributes (10 parameters) can cause accuracy instability and increase computational load. Therefore, Correlation-based Feature Selection (CFS) was applied to reduce redundant features. The CFS process filtered the initial 10 features into 4 selected features (Flavor, Acidity, Cupper Points, and Aroma). The Dataset was obtained from the Kaggle Coffee Quality Database and had undergone Excel format adjustments before being loaded into the system. Modeling was conducted using 1,303 data records with a 30% split for testing data. The evaluation results showed that the Naive Bayes algorithm provided the best performance, achieving an accuracy rate of 97.95%. The use of CFS proved successful in reducing feature dimensions by 60% without significantly decreasing classification accuracy. Keywords: Arabica Coffee; Export Classification; K-Nearest Neighbor; Naive Bayes; Correlation-based Feature Selection.   Abstrak: Kopi Arabika merupakan komoditas ekspor bernilai tinggi bagi perekonomian Indonesia. Untuk menjaga daya saing di tingkat global, biji kopi harus memenuhi standar kualitas kelayakan ekspor berdasarkan cupping score dari Coffee Quality Institute (CQI). Penelitian ini bertujuan membandingkan kinerja algoritma K-Nearest Neighbor (KNN) dan Gaussian Naive Bayes dalam klasifikasi kelayakan ekspor biji kopi Arabika. Dalam pengujian sensoris mutu, dimensionalitas atribut yang tinggi (10 parameter) dapat menyebabkan ketidakstabilan akurasi dan meningkatkan beban komputasi. Oleh karena itu, Correlation-based Feature Selection (CFS) diterapkan untuk mereduksi fitur redundan. Proses CFS menyaring 10 fitur awal menjadi 4 fitur terpilih (Flavor, Acidity, Cupper Points, Aroma). Dataset diambil dari Kaggle Coffee Quality Database dan telah melalui tahapan penyesuaian format Excel sebelum dimuat ke sistem. Pemodelan dilakukan menggunakan 1303 rekam data dengan pembagian data uji sebesar 30%. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes memberikan performa terbaik dengan tingkat akurasi mencapai 97,95%. Penggunaan CFS terbukti berhasil memangkas dimensi fitur sebesar 60% tanpa menurunkan akurasi klasifikasi secara signifikan. Kata Kunci: Kopi Arabika; Klasifikasi Ekspor; K-Nearest Neighbor; Naive Bayes; Correlation-based Feature Selection.
TREN, TANTANGAN, DAN ARAH PENELITIAN PEMANFAATAN MACHINE LEARNING PADA BERBAGAI SEKTOR DI ERA ARTIFICIAL INTELLIGENCE: SEBUAH SYSTEMATIC LITERATURE REVIEW TAHUN 2020–2026 Sudy; Dita Mitha Sucitra; Aulianza Alfirzy Saragih; Novaldo Andrian Syahputra; Dicky Apdillah
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6688

Abstract

Abstract: The advancement of Artificial Intelligence (AI) has driven the widespread adoption of Machine Learning (ML) across various sectors. This study aims to analyze the development trends, implementation sectors, dominant algorithms, key challenges, and future research directions of ML during the 2020–2026 period. Utilizing a Systematic Literature Review (SLR) based on PRISMA 2020 and Kitchenham Guidelines, 120 peer-reviewed articles from six international academic databases were systematically evaluated. The findings indicate a significant increase in ML publications post-Generative AI era, with the highest adoption rates observed in the healthcare, finance, and manufacturing sectors. Algorithms such as Random Forest, XGBoost, CNN, and LSTM dominate for prediction and classification tasks. Despite its benefits, the primary challenges of ML deployment involve data quality, privacy, algorithmic bias, and model interpretability. This study highlights a shifting research paradigm toward transparent and human-centered AI, outlining future agendas in Explainable AI (XAI), Federated Learning, and Green AI. Keywords: Artificial Intelligence; Explainable AI; Federated Learning; Generative AI; Machine Learning.   Abstrak: Perkembangan Artificial Intelligence (AI) mendorong pemanfaatan Machine Learning (ML) secara luas di berbagai sektor lintas disiplin. Penelitian ini bertujuan menganalisis tren perkembangan, sektor implementasi, algoritma dominan, tantangan utama, serta arah penelitian masa depan terkait pemanfaatan ML selama periode 2020–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) berbasis pedoman PRISMA 2020 dan Kitchenham Guidelines terhadap 120 artikel ilmiah terpilih dari enam database akademik internasional. Hasil tinjauan menunjukkan adanya peningkatan signifikan publikasi ML pasca-era Generative AI, dengan adopsi tertinggi pada sektor kesehatan, keuangan, dan manufaktur. Algoritma berbasis data tabular (seperti Random Forest dan XGBoost) serta data citra/deret waktu (seperti CNN dan LSTM) menjadi yang paling dominan digunakan untuk kebutuhan prediksi dan klasifikasi. Meskipun menawarkan efisiensi tinggi, tantangan utama implementasi ML berpusat pada kualitas data, privasi, bias algoritma, dan interpretabilitas model. Penelitian ini merumuskan agenda riset masa depan yang mulai bergeser ke arah pengembangan AI yang transparan dan berpusat pada manusia, seperti Explainable AI (XAI), Federated Learning, dan Green AI. Kata Kunci: Artificial Intelligence; Explainable AI; Federated Learning; Generative AI; Machine Learning.
SYSTEMATIC LITERATURE REVIEW TERHADAP SEMANTIC PARSING BERBASIS TRANSFORMER DALAM NATURAL LANGUAGE PROCESSING Dwi Suci Ramadani; Fahira Hasanah Br Harahap; Suci Lestari Br Batu Bara; Winda Sari; Dicky Apdillah
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6689

Abstract

Abstract: The development of transformer architecture has brought significant changes in Natural Language Processing (NLP) research, particularly in semantic parsing tasks. This study aims to analyze the development of transformer-based semantic parsing through a Systematic Literature Review (SLR) approach. The research method uses the PRISMA guidelines with a process of identification, selection, and analysis of articles from the Scopus, ScienceDirect, IEEE Xplore, Springer, and Google Scholar databases for the period 2019–2026. The results of the study show that transformer models such as BERT, T5, GPT, and transformer encoder–decoder have become the dominant approaches in modern semantic parsing because they are able to improve contextual understanding, semantic representation, and reasoning more effectively than previous methods. The most widely used datasets include Spider, WikiSQL, GeoQuery, ATIS, and Overnight, with the main evaluation being Execution Accuracy and Exact Match Accuracy. This study also identifies various challenges, such as limited labeled data, high computational costs, low model interpretability, the risk of hallucinations, and limitations in low-resource languages. This research contributes to a literature review, identifying research gaps, and recommending future research directions for transformer-based semantic parsing. Keywords: BERT, Natural Language Processing, Semantic Parsing, Systematic Literature Review, Transformer, T5, Text-to-SQL.   Abstrak: Perkembangan arsitektur transformer telah membawa perubahan signifikan dalam penelitian Natural Language Processing (NLP), khususnya pada tugas semantic parsing. Penelitian ini bertujuan untuk menganalisis perkembangan semantic parsing berbasis transformer melalui pendekatan Systematic Literature Review (SLR). Metode penelitian menggunakan pedoman PRISMA dengan proses identifikasi, seleksi, dan analisis artikel dari database Scopus, ScienceDirect, IEEE Xplore, Springer, dan Google Scholar pada periode 2019–2026. Hasil kajian menunjukkan bahwa model transformer seperti BERT, T5, GPT, dan encoder–decoder transformer menjadi pendekatan dominan dalam semantic parsing modern karena mampu meningkatkan contextual understanding, semantic representation, dan reasoning secara lebih efektif dibandingkan metode sebelumnya. Dataset yang paling banyak digunakan meliputi Spider, WikiSQL, GeoQuery, ATIS, dan Overnight, dengan metrik evaluasi utama berupa Execution Accuracy dan Exact Match Accuracy. Penelitian ini juga mengidentifikasi berbagai tantangan, seperti keterbatasan data berlabel, tingginya biaya komputasi, rendahnya interpretabilitas model, risiko hallucination, serta keterbatasan pada low-resource language. Penelitian ini berkontribusi dalam memberikan pemetaan literatur, identifikasi research gap, dan rekomendasi arah penelitian semantic parsing berbasis transformer pada masa mendatang. Kata Kunci: BERT, Natural Language Processing, Semantic Parsing, Systematic Literature Review, Transformer, T5, Text-to-SQL.
Co-Authors Afni Dwi Pertiwi Agrinda Aulia Lubis Alwi Kurniawan Amira Harisatul Zannah Anggika Sembiring Annisa Nasution Apri Affandi Arinda Faradilla Marpaung Aulianza Alfirzy Saragih Bambang Irwansyah Bobby Ardiansyah Dea Tiara Azhari Delyanti Putri Sitorus Desi Patmala Marpaung Desy Rahmadani Dinda Azura Panjaitan Dinda Munifah Marpaung Dini Farhatun Dita Mitha Sucitra Diva Agustin Purba Dormada Lestari Luhur Sitorus Dwi Suci Ramadani Eko Bayu Syahputra Emi Dea Emiel Salim Siregar Fahira Hasanah Br Harahap Fahri Finanda Rizki Harmayani Harmayani Husna Sari Ibnu Anhar Icha Wayu Mayanda Imelda Regina Siswi Intan Ayudia Kinanti Irwansyah, Bambang Ismail Joshua Robinsar Tamba Laura Gusti Ayunda M. Affandi Saragih M.Rajuddin Saragih Mhd Fauzan Mohammad Zacky Valentino Putra Muhammad Idham Nabila Nabila Nadia Syharani Nazma Aulia Novaldo Andrian Syahputra Nurul Fadillah Pani Irawan Pujawati Puteri Leida Ratna Hayati Harahap Putra Bagus Utama Putri Julia Nabila Putri Nadila Putri Putri Putri Rahmawani Rahmadani Fitri Panjaitan Rahmat Raja Syahmuda Siregar Reni Surya Nanda Ridho Agusman Rienda Syuhaila Riki Wirayuda Afni Dwi Pertiwi Riski Ananda Riski Perdamaian Ndraha Rismawani Butar-butar Rizky Riansyah Panjaitan Rusti Br Zebua Sahdila Adinda Putri Selfina Agustin Seprina Aulia Putri Shohe Mukhreza Sinta Widari Siti Ramadani Sri Bintan Sri Damayanti Sri Wulandari Suci Lestari Br Batu Bara Suci Ramadani Sudy Sulhani Nuraini Syahrunsyah Syahrunsyah Tia Rizkika Utami Wardah Hafiz Weny Nur Afdilla Simangunsong Winda Sari Yudha Rahmadhi Yuwanda Yuwanda Zhalila Azka Zidan Harry Sudrajat Zulfa Ar Rahman Zulfirman