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All Journal PROSIDING SENATEK FAKULTAS TEKNIK UMP INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal SOLMA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) Multitek Indonesia : Jurnal Ilmiah KOMPUTEK JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing GANESHA: Jurnal Pengabdian Masyarakat Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Sains Komputer dan Sistem Informasi Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Yovi Litanianda, Yovi
Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Ponorogo Jl. Budi Utomo No 10 Ponorogo, 63471
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation Other

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Fuzzy Method Design for IoT-Based Mushroom Greenhouse Controlling Prasetyo, Angga; Setyawan, Moh. Bhanu; Litanianda, Yovi; Sugianti, Sugianti; Masykur, Fauzan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 1 (2022): February 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.632 KB) | DOI: 10.29407/intensif.v6i1.16786

Abstract

The ideal conditions for the oyster mushrooms growth are at a humidity of 65-75% and 29-31C during incubation, while the growth of stems should be at a humidity of 70-90% 29-32C. This ideal ecosystem is maintained by aeration and manual watering. Still, the results are not optimal in preventing damage to the mycelium during the incubation period, resulting in a decrease in crop yields. Automatic control has not created ideal conditions because air temperature and humidity regulation are only based on fans and sprayers that do not directly affect air conditions. Therefore, we need a method to manipulate the mushroom greenhouse space ecosystem, namely fuzzy logic, the application of fuzzy logic integrated with sensors, actuators, and microcontrollers with the Internet of Things to solve this problem. The results of the installation of fuzzy logic in the mushroom's greenhouse in this system can be seen from the fan's modulation response and the pump's duration. The test results of this control feature can manipulate temperature and humidity. Therefore, the oyster mushroom greenhouse produces an ideal state of 29.8C, the humidity of 68.97% RH, and the production has been proven to be optimal with an average daily harvest of 3.8kg.
Pengendalian Suhu dan Kelembapan Kumbung Jamur Dengan Metode Fuzzy Terintegrasi Internet of Things Prasetyo, Angga; Litanianda, Yovi; Setyawan, Moh. Bhanu; Masykur, Fauzan; Sugianti, Sugianti; Sumaji, Sumaji
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 5 No. 1 (2021): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v5i1.841

Abstract

Jamur tiram atau dalam bahasa latin volvariella volvacea budidaya jamur tiram ini, membutuhkan akurasi dan toleransi kepresisian dalam mengendalikan suhu serta kelembapan yang menyerupai ekosistem habitat jamur tiram sebenarnya, fase inkubasi yang membutuhkan suhu udara 23-28C dengan kelembapan 60- 70%, Fase pembentukan Tubuh dan buah membutuhkan suhu udara 28-32C dengan kelembapan 70-90%. Pengelolaan suhu udara dan kelembapan oleh pembudidaya jamur tiram dilakukan dengan cara penyemprotan serta aerasi kumbung yang masih manual, sehingga pada tahapan fase inkubasi dan fase pembentukan tubuh jamur, belum optimal. Akibatnya hasil panen jamur menurun karena banyak miselium yang rusak saat fase inkubasi. perancangan system akan dilakukan dalam dua tahapan, fase pertama pembuatan wiring perangkat keras, kemudian fase kedua pengintegrasian logika fuzzy di perangkat lunak yang secara keseluruhan akan berupa internet of things (IoT) guna memudahkan dalam proses monitoring. Kinerja logika fuzzy pada sistem ini dilihat dari respon PWM kipas, durasi pompa dan kualitas jaringan pada koneksi internetnya. Hasil pengujian menunjukkan nilai PWM kipas berhasil merespon berbagai kondisi suhu. Durasi penyalan pompa juga bisa merespon perubahan kelembaban ruangan jamur. Sedangkan kualitas jaringan dari hasil percobaan diperoleh nilai konektifitas berupa nilai jitter buffering data 0,72 ms, nilai ping jaringan saat kondisi transmitter(Tx) dan received (Rx) 0,29 ms, dan delay sebesar 0,97 ms atau secara keseluruhan rata-ratanya kurang dari 1ms merupakan kondisi yang termasuk baik untuk penyelenggaraan sistem IoT.
Pengembangan Sistem Pendukung Keputusan Berbasis Machine Learning untuk Prediksi Kinerja Dosen Menggunakan Data Historis Evaluasi Pembelajaran Z, Ismail Abdurrozzaq; Widaningrum, Ida; Litanianda, Yovi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9363

Abstract

Lecturer performance evaluation is a crucial component in efforts to improve the quality of higher education. However, traditional evaluation methods still face various challenges, such as subjective assessments, a lack of consistent standards, and lengthy decision-making processes. These conditions highlight the need for a more measurable, accurate, and data-driven evaluation mechanism, particularly in the context of ongoing digital transformation. This study aims to design and develop a lecturer performance prediction system using a machine learning (ML) approach within a Decision Support System (DSS) framework. The research approach involves processing historical lecturer data covering aspects of Teaching (including student evaluation scores, instructional innovation, and attendance levels), Research (number of publications, H-index, and participation in academic conferences), Community Service, and other administrative activities. Predictive models were developed and compared using several machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and XGBoost. Experimental results show that Random Forest achieved an accuracy of 88.0%, SVM 85.0%, and MLP 87.0%, while XGBoost demonstrated the best performance with an accuracy of 92.0%, precision of 91.0%, recall of 90.0%, and an F1-score of 91.0%. Based on these results, XGBoost was selected as the primary model for the DSS. In addition, the system is equipped with a rule-based module that generates follow-up recommendations based on the model’s prediction results. All system components are implemented in an interactive dashboard using the Streamlit framework, enabling users to input data, monitor prediction outcomes, and obtain decision recommendations in a fast and data-driven manner.
Co-Authors Abdurrozzaq Z, Ismail Abdurrozzaq, Ismail Aditya, Charisma Wahyu Alfiansyah, Nanda Safiq Angga Prasetyo Apristawijaya, Oceu Arifqi, Miftahul Asmaul Khusna, Zakia Access Astuti, Arin Yuli Bimantoro, Alfin Dien Edy Kurniawan, Edy Fadila, Daras Fajaryanto C, Adi Fajaryanto, Adi Fauda Pradana, Yofhi Fauzan Masykur Fikrotul Ulya As Siddieq, Muhammad Firdaus, Usamah Putra Fitriani, Lely Mustikasari Mahardhika Ida Widaningrum, Ida Ishaq Kaffah Imanuddin M R Santosa Karaman, Jamilah KUNTANG WINANGUN Mohammad Bhanu Setyawan Muhammad Ridwan Hidayatulloh, Muhammad Ridwan Nashikhuddin, Ahmad Yahya Renaldy, Aldi Rizki Dwi Ardika Rizki, Rizki Dwi Ardika Safiq Alfiansyah, Nanda Setyawan, Moh Bhanu Setyawan, Moh. Bhanu Sopian sopian Sugianti Sugianti, Sugianti Sumaji Tama, Okti Liksyan Nita Wijayanti, Zahrotul Ilmi Winardi, Yoyok Yuli Astuti, Arin Yusuf, Arief Rahman Z, Ismail Abdurrozzaq Zulkarnain, Ismail Abdurrazzaq Zulkarnain, Ismail Abdurrozzaq