Optimalisasi Fuzzy Logic System Dalam Identifikasi Kematangan Buah Semangka
DOI:
https://doi.org/10.36050/280dyy31Keywords:
desibel, fuzzy logic, optimalization, watermelon, harves ageAbstract
In watermelon cultivation, determining the right time to harvest is very important because it will
affect the quality and selling value. Generally, farmers use various methods to determine the ripeness of
watermelon, such as observing changes in skin color, tapping the fruit to listen to the sound it makes, and
estimating the age of harvest based on the planting date. Although these methods have been used for many
years by farmers, they rely heavily on experience and intuition, which can result in high variability and
uncertainty in determining maturity. Modern technological developments offer more scientific and accurate
solutions. One approach that can be used is to use fuzzy logic. Fuzzy logic is a computational method that
is able to handle uncertainty and imprecision, which is very suitable for applications in the agricultural
sector where many variables are difficult to measure with certainty. This research will examine how harvest
age and tapping sound can be integrated in a fuzzy logic system to determine the ripeness of watermelon
more scientifically and accurately. The research results show that the implementation of the fuzzy logic
system in optimizing the identification of watermelon ripeness was successfully integrated into a programming language to produce a software with fuzzy algorithm method stages based on two parameter
variables, namely harvest age and the sound produced when a watermelon is tapped (decibels).
References
[1] P. A. Sari, K. Wibowo, and I. Tjolli, “Produksi Dan Pemasaran Buah Semangka (Citrullus Lanatus) DI Kampung Sumber Boga Distrik Masni Kabupaten Manokwari,” Sosio Agri Papua, vol. 13, no. 2, pp. 148–157, Dec. 2024, doi: 10.30862/sap.v13i2.401.
[2] I. Jangcik, Sasmita, M. B. Billah, and Yuhelmi, “Penerapan Metode K-Nearest Neigbhor Klasifikasi Jenis Buah Semangka Dengan Image Processing,” ZONAsi J. Sist. Inf., vol. 5, no. 3, Dec. 2023, Accessed: Aug. 07, 2025. [Online]. Available: https://journal.unilak.ac.id/index.php/zn/article/view/16301
[3] S. Salsabilla, I. Nirmala, and T. Rismawan, “Sistem Pemilah Otomatis Tingkat Kematangan Buah Kelapa Sawit Menggunakan Metode Logika Fuzzy Mamdani Dan Sensor TCS3200,” J. Comput. Syst. Inform. JoSYC, vol. 5, no. 1, pp. 144–154, Nov. 2023, doi: 10.47065/josyc.v5i1.4449.
[4] Q. A’yun and F. Utaminingrum, “Rancang Bangun Deteksi Kemanisan Buah Semangka menggunakan Metode Gray Level Co-Occurrence Matrix dan Backpropagation Neural Network Berbasis Raspberry Pi,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 6, no. 2, pp. 707–712, Jan. 2022.
[5] M. A. Ikhsanudin, E. Setyati, and H. Junaedi, “Deteksi Tingkat Kemanisan Buah Semangka (Citrullus Lanatus) Berdasarkan Ciri Kulit BUah Dengan Menggunakan Metode CNN (Convolutional Neural Network),” JIPI J. Ilm. Penelit. Dan Pembelajaran Inform., vol. 8, no. 4, pp. 1501–1513, Dec. 2023, doi: 10.29100/jipi.v8i4.4118.
[6] N. Sakinah, K. Bariyyah, and A. Hadi, “Respon Perkembangan Buah pada Tanaman Semangka terhadap Pemberian Asam Humat sebagai Dasar Budidaya Smart Farming,” Agrotekma J. Agroteknologi Dan Ilmu Pertan., vol. 7, no. 2, pp. 72–81, June 2023, doi: 10.31289/agr.v7i2.8480.
[7] B. R. Setya, M. Darip, and A. A. Sayyidah, “Perancangan Aplikasi Pengajuan Cuti Berbasis Android Di Rumah Sakit Umum Ibunda Serang,” Innov. J. Soc. Sci. Res., vol. 4, no. 1, Art. no. 1, Jan. 2024, doi: 10.31004/innovative.v4i1.8444.
[8] M. Darip and S. Auliana, “Aplikasi Pencarian Lokasi Kost Bagi Mahasiswa Di Kota Serang Menggunakan Algoritma UCS,” J. TEKINKOM, vol. 7, no. 2, 2024, doi: 10.37600/tekinkom.v7i2.1419.
[9] F. H. Wardhani and K. M. Lhaksmana, “Predicting Employee Attrition Using Logistic Regression With Feature Selection,” Sink. J. Dan Penelit. Tek. Inform., vol. 6, no. 4, pp. 2214–2222, Oct. 2022, doi: 10.33395/sinkron.v7i4.11783.
[10] A. Nurmasani and Y. Pristyanto, “Algoritme Stacking Untuk Klasifikasi Penyakit Jantung Pada Dataset Imbalanced Class,” Pseudocode, vol. 8, no. 1, pp. 21–26, Mar. 2021, doi: 10.33369/pseudocode.8.1.21-26.
[11] A. Kamsyakawuni, A. Riski, and A. B. Khumairoh, “Application Fuzzy Mamdani To Determine The Ripeness Level of Crystal Guava Fruit,” BAREKENG J. Ilmu Mat. Dan Terap., vol. 16, no. 3, pp. 1087–1096, Sept. 2022, doi: 10.30598/barekengvol16iss3pp1087-1096.
[12] M. F. Amin, S. R. Akbar, and E. R. Widasari, “Rancang Bangun Sistem Sortir Buah Apel Menggunakan Sensor Warna Dan Sensor Suhu,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 1, no. 3, pp. 236–240, May 2017.
[13] A. Amriana, A. A. Kasim, and M. Maghfirat, “Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic,” Ilk. J. Ilm., vol. 12, no. 3, pp. 236–244, 2020, doi: 10.33096/ilkom.v12i3.619.236-244.
[14] U. M. Rifanti, H. Pujiharsono, and Z. H. Pradana, “Implementasi Logika Fuzzy Pada Penilaian Kegiatan Merdeka Belajar Kampus Merdeka (MBKM),” JST J. Sains Dan Teknol., vol. 12, no. 1, pp. 250–260, Mar. 2023, doi: 10.23887/jstundiksha.v12i1.50057.
[15] R. Reynaldi, W. Syafrizal, and M. F. A. Hakim, “Analisis Perbandingan Akurasi Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Dalam Prediksi Penentuan Harga Mobil Bekas,” Indones. J. Math. Nat. Sci., vol. 44, no. 2, pp. 73–80, Oct. 2021, doi: 10.15294/ijmns.v44i2.32967.
[16] M. Rezani, L. Fatah, and L. Anjardiani, “Analisis Usahatani Semangka Di Kecamatan Matraman Kabupaten Banjar,” Front. Agribisnis, vol. 8, no. 1, pp. 158–166, Mar. 2024, doi: 10.20527/frontbiz.v8i1.12275.
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