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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JurTI (JURNAL TEKNOLOGI INFORMASI) JOURNAL OF SCIENCE AND SOCIAL RESEARCH J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal IPTEK Bagi Masyarakat Sistem Pendukung Keputusan dengan Aplikasi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Bigint Computing Journal Jurnal Garuda Pengabdian Kepada Masyarakat Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Cosmic Jurnal Teknik Jurnal Teknik Informatika dan Teknologi Informasi
Helmi Fauzi Siregar
Program Studi Teknik Informatika Universitas Asahan
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA FUZZY C-MEANS DALAM PENGELOMPOKAN SKALA PRODUKSI PABRIK KELAPA SAWIT Daniswara; Helmi Fauzi Siregar
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.v9i2.6261

Abstract

Abstract: Recording production output is one of the key activities in the oil palm production management system at PT Socfindo Kebun Aek Loba. However, the production data generated is still presented solely as production figures without any categorization by production scale, necessitating further data processing to identify production scales more systematically. Therefore, this study aims to design and develop a clustering application using the Fuzzy C-Means (FCM) algorithm based on PHP and MySQL to group the levels of oil palm production at PT Socfindo Kebun Aek Loba. The data used in this study were obtained from the archives and production reports of the oil palm mill at PT Socfindo Kebun Aek Loba. A total of 211 palm oil production data points were obtained from PT Socfindo Kebun Aek Loba. Based on the results of the system’s computational testing, the Fuzzy C-Means algorithm successfully achieved convergence and objectively clustered the data into three clusters. Of the total 211 processed data points, 42 production data points were classified into the Low Production cluster (C1), 75 into the Medium Production cluster (C2), and 94 into the High Production cluster (C3). The benefit of this study is that it helps provide clear, precise, and accurate information regarding the classification of oil palm production scales at PT Socfindo Kebun Aek Loba. Keywords: Clustering, Data Mining, Production Scale, Palm Oil Mill, Fuzzy C-Means.   Abstrak: Pencatatan hasil produksi merupakan salah satu kegiatan penting dalam sistem pengelolaan produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Namun, data produksi yang dihasilkan masih disajikan dalam bentuk angka produksi tanpa adanya pengelompokkan skala produksi kelapa sawit, sehingga diperlukan pengolahan data lanjutan agar skala produksi dapat diidentifikasi secara lebih sistematis. Oleh karena itu, penelitian ini bertujuan untuk merancang dan membangun aplikasi clustering menggunakan algoritma Fuzzy C-Means (FCM) berbasis PHP dan MySQL untuk mengelompokkan tingkat produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Data yang digunakan dalam penelitian ini merupakan data yang diperoleh dari arsip dan laporan produksi pabrik kelapa sawit di PT Socfindo Kebun Aek Loba. Data yang didapat sebanyak 211 data produksi kelapa sawit di PT Socfindo Kebun Aek Loba.  Berdasarkan hasil pengujian komputasi sistem, algoritma Fuzzy C-Means berhasil mencapai konvergensi dan mengelompokkan data kedalam tiga klaster secara objektif. Dari total 211 data yang diolah, didapatkan hasil sebanyak 42 data produksi masuk ke dalam klaster Produksi Rendah (C1), 75 data produksi masuk ke dalam klaster Produksi Sedang (C2), 94 data produksi masuk ke dalam klaster Produksi Tinggi (C3). Manfaat dari penelitian ini adalah membantu dalam memperoleh informasi yang jelas, tepat dan akurat mengenai pengelompokkan skala produksi kelapa sawit di PT Socfindo Kebun Aek Loba. Kata Kunci: Clustering, Data Mining, Skala Produksi, Pabrik Kelapa Sawit, Fuzzy C-Means.
ANALISIS KEPUTUSAN DALAM PENENTUAN MENU FAVORIT PADA PROGRAM MAKAN BERGIZI BERDASARKAN PREFERENSI RASA DAN KANDUNGAN GIZI MENGGUNAKAN METODE FUZZY MAMDANI Fahrizal; Helmi Fauzi Siregar
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.v9i2.6263

Abstract

Abstract: The success of a nutritious meal program for school children depends heavily on the balance between objective fulfillment of nutritional standards and subjective taste acceptability. Mistakes in menu preparation often result in high food waste due to unpopular taste, despite its high nutritional content. This study aims to conduct a scientific decision analysis in evaluating and determining the feasibility of a food menu through a web-based system interface. The evaluation was analyzed using the Mamdani fuzzy logic algorithm by processing two main input variables, namely the composite nutritional score and the hedonic score. The computational stages of the analysis include fuzzification, MIN implication rule inference, MAX curve aggregation, and defuzzification using the Center of Area (CoA) method based on the area-area continuous integral. The results of the functional testing showed that all computational modules operated 99% validly. This decision analysis successfully determined the final feasibility percentage and classified the menu into three statuses: Recommended, Considered, and Rejected, thus facilitating the determination of favorite menus in a measurable and objective manner. Keywords: Decision Analysis, Fuzzy Mamdani, Nutritious Meal, Hedonic Score, Nutritional Adequacy Rate.   Abstrak: Keberhasilan program makan bergizi bagi anak sekolah sangat bergantung pada keseimbangan antara pemenuhan standar nutrisi secara objektif dan tingkat penerimaan rasa secara subjektif. Kesalahan dalam menyusun menu seringkali berdampak pada tingginya sisa makanan akibat rasa yang kurang disukai, meskipun kandungan gizinya tinggi. Penelitian ini bertujuan untuk melakukan analisis keputusan secara ilmiah dalam mengevaluasi dan menentukan kelayakan menu makanan melalui sebuah antarmuka sistem berbasis web. Evaluasi dianalisis menggunakan algoritma logika fuzzy mamdani dengan mengolah dua variabel input utama, yaitu skor gizi komposit dan skor hedonik. Tahapan komputasi analisis meliputi proses fuzzifikasi, inferensi aturan implikasi MIN, agregasi kurva MAX dan defuzzifikasi menggunakan metode Center of Area (CoA) berbasis integral kontinu luas area. Hasil pengujian fungsionalitas menunjukkan bahwa seluruh modul komputasi beroperasi 99% valid. Analisis keputusan ini berhasil menetapkan persentase kelayakan akhir dan mengklasifikasikan menu ke dalam tiga status: Direkomendasikan, Dipertimbangkan dan Ditolak sehingga mampu memfasilitasi penentuan menu favorit secara terukur dan objektif. Kata Kunci: Analisis Keputusan, Fuzzy Mamdani, Makan Bergizi, Skor Hedonik, Angka Kecukupan Gizi.
IMPLEMENTASI ALGORITMA K-MEANS DALAM PENGELOMPOKAN DOSIS PEMUPUKAN KELAPA SAWIT BERDASARKAN KONDISI TANAMAN Fajar Hardiansyah; Helmi Fauzi Siregar
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.v9i3.6578

Abstract

Oil palm is one of the plantation commodities that plays an important role in improving Indonesia's economy. PT Socfindo Kebun Aek Loba has a large amount of oil palm plant condition data; however, the data has not been optimally utilized to determine fertilizer dosage requirements. This study aims to classify oil palm fertilizer dosage requirements based on plant conditions using the K-Means algorithm and to design an application that supports the clustering process. The variables used in this study include plant age, tree height, number of fruit bunches, and number of fronds. The dataset consisted of 200 oil palm plant records. The clustering process was carried out by forming three clusters, namely low, medium, and high fertilizer dosage groups, using the K-Means method with Euclidean Distance calculations. The results showed that out of 200 plant data records processed, 92 data (46%) were classified into the low fertilizer dosage cluster, 54 data (27%) into the medium fertilizer dosage cluster, and 54 data (27%) into the high fertilizer dosage cluster. In addition, this study successfully developed an application using PHP and MySQL that is capable of managing data, performing clustering automatically, and presenting clustering results in the form of tables and charts. The developed application can assist PT Socfindo Kebun Aek Loba in obtaining information regarding fertilizer dosage requirements more quickly, effectively, and systematically, thereby supporting decision-making related to oil palm fertilization.
Co-Authors Adi Widarma, Adi Al Ayyub, Muhammad Azwar Arfah Anggina Daniswara Fahrizal Fajar Hardiansyah Fasya, Muhammad Rezeki Firman, Mulia Heri Setiawan Ikhsan, M. Audi Irawan, M Dedi K Kartika Khairul Saleh Khairul Saleh Khairunnisa Khairunnisa Liana, Tika M. Faisal Afiff Tarigan Maulana, Mhd Tantowi Melani Melani Mhd Addurrun Nafis Mhd Tantowi Maulana Muhammad Dedi Irawan Muhammad Yasin Simargolang Muhammas Yasin Simargolang Nadia Salsabilla Nilfa Sari Parinduri, Ikhsan Pauji Wibowo Rangkuti, Faiz Wahyu Perdana Sandi Sahputra Simargolang, Muhammad Yasin Sitorus, Indi Ramadhani Suhermanto Suhermanto Suhermanto, Suhermanto Taufik Qurrahman Tika Liana Wini Istya Sari Lubis Wini Yustria Handika Siregar Zunaida Sitorus