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All Journal Techno.Com: Jurnal Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Inspiration Jurnal Komputer Terapan Indonesian Journal of Artificial Intelligence and Data Mining Insect (Informatics and Security) : Jurnal Teknik Informatika SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jambura Journal of Informatics EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Dedikasi Journal of Computer and Information System (J-CIS) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Pengabdian Masyarakat UNIPOL J-Icon : Jurnal Komputer dan Informatika Nobel Community Services Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Pengabdian Kepada Masyarakat Indonesia
Farid Wajidi
Universitas Sulawesi Barat
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Public Health Social Sciences Transportation Other

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Prediksi Harga Gabah Kering Panen menggunakan Metode Autoregressive Integrated Moving Average Ria, Canda; Wajidi, Farid; Nur, Nahya
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8614

Abstract

Gabah kering panen (GKP) merupakan komoditas pertanian strategis yang berperan signifikan dalam mendukung ketahanan pangan nasional. Fluktuasi harga gabah yang tidak menentu menjadi tantangan serius bagi petani dalam menentukan harga jual hasil panen. Dalam penelitian ini, harga gabah kering panen diprediksi dengan menggunakan metode   Autoregressive Integrated Moving Average (ARIMA). Data yang digunakan diambil dari data bulanan yang dikumpulkan oleh Badan Pusat Statistik (BPS) dari januari 2010 hingga desember 2024. Proses penelitian meliputi tahap pengumpulan data, pengolahan data, uji stasioneritas menggunakan Augmented Dickey-Fuller (ADF), analisis Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF), pemilihan parameter optimal (p, d, q) menggunakan pendekatan grid search, pembangunan model ARIMA, prediksi, dan mengevaluasi performa model. Hasil uji ADF menunjukkan bahwa data menjadi stasioner setelah differencing kedua. Berdasarkan hasil grid search dan nilai Akaike Information Criterion (AIC) yang paling rendah. Dengan nilai AIC, model yang paling cocok adalah ARIMA (1,2,2). Sebesar 2188,98. Evaluasi terhadap nilai Mean Absolute Percentage Error (MAPE) sebesar 8,90%, Root Mean Squared Error (RMSE) sebesar 636,97 dan Mean Squared Error (MSE) sebesar 405731,65. Dari hasil penelitian ini, dapat disimpulkan bahwa model ARIMA cukup andal dan akurat dalam memprediksi harga gabah kering panen.
SISTEM MONITORING KUALITAS AIR PADA TAMBAK IKAN BANDENG MENGGUNAKAN FUZZY SUGENO Quraisy, Imam; Rusman, Ismaun; Hidayat, Fahril; Wajidi, Farid
Journal of Computer and Information System ( J-CIS ) Vol 8 No 2 (2025): J-CIS Vol. 8 No. 2 Tahun 2025
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v8i2.5588

Abstract

Kualitas udara merupakan faktor krusial dalam budidaya ikan bandeng (Chanos chanos) untuk mendukung pertumbuhan optimal sekaligus meminimalkan risiko kematian. Fluktuasi parameter utama seperti pH, suhu, kekeruhan, dan Total Dissolved Solids (TDS) pada tambak menjadi tantangan yang memerlukan sistem pemantauan Andal. Penelitian ini bertujuan merancang sistem pemantauan kualitas udara berbasis Internet of Things (IoT) dengan metode Fuzzy Sugeno. Sistem ini mengintegrasikan sensor-sensor yang terhubung ke NodeMCU untuk membaca parameter udara, kemudian memproses data menggunakan logika fuzzy guna mengklasifikasikan kondisi udara menjadi kategori LAYAK atau TIDAK LAYAK. Hasil implementasi menunjukkan bahwa sistem mampu menyajikan informasi secara real-time melalui aplikasi Android, sehingga mendukung petambak dalam pengambilan keputusan yang cepat dan tepat. Namun akurasi sensor pH masih perlu ditingkatkan agar hasil pemantauan lebih optimal. Dengan demikian, sistem ini berpotensi menjadi solusi teknologi yang efektif dalam mendukung akuakultur berkelanjutan serta mengurangi risiko kerugian ekonomi pada budidaya bandeng.
Implementasi Algoritma Monte Carlo dengan Data Fluktuatif untuk Prediksi Jumlah Tamu di Penginapan Wirawan, I Putu Andreana; Wajidi, Farid; Sulfayanti, Sulfayanti
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14475

Abstract

Penginapan berperan penting dalam mendukung mobilitas masyarakat, baik untuk kepentingan bisnis, pekerjaan, maupun perjalanan pribadi, sekaligus berkontribusi dalam sektor pariwisata sebagai sarana akomodasi. Salah satu tantangan utama penginapan adalah ketidakpastian jumlah tamu bulanan yang berdampak pada pendapatan dan pemanfaatan kamar. Penelitian ini bertujuan membangun model prediksi jumlah tamu tahun 2025 dengan metode simulasi Monte Carlo menggunakan data historis 2022–2024. Tahapan simulasi meliputi pembentukan distribusi probabilitas, penyusunan distribusi kumulatif, penentuan interval bilangan acak, serta pembangkitan bilangan acak melalui Mixed Congruential Method. Hasil penelitian menunjukkan pola musiman yang konsisten, dengan puncak kunjungan pada April yang diduga terkait perayaan Idulfitri 1446 Hijriah. Model prediksi mencapai akurasi 76,22% pada 2023, 76,80% pada 2024, dan 88,86% pada 2025, dengan rata-rata 80,63%. Pada 2025 diperoleh nilai MAE sebesar 88,50, RMSE sebesar 128,91, dan MAPE sebesar 11,14%. Temuan ini menegaskan bahwa metode Monte Carlo efektif dalam merepresentasikan ketidakpastian serta relevan sebagai dasar pengambilan keputusan strategis, sehingga dapat direkomendasikan untuk memprediksi lonjakan jumlah tamu dan mendukung peningkatan kualitas layanan penginapan.   Kata kunci – Jumlah tamu, prediksi, monte carlo, simulasi, data historis.
Pemanfaatan Sistem Informasi Edukasi Mitigasi Bencana pada KSR PMI Unit Markas Majene Nahya Nur; Farid Wajidi; Ade Mulawarman; Muh. Adrian; Rifky Gunawan
Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Vol. 2 No. 3 (2022): Desember : Jurnal Pengabdian Kepada Masyarakat Indonesia (JPKMI)
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpkmi.v2i3.726

Abstract

The availability of information related to disasters is one of the efforts so that the public better understands the actions that must be taken to minimize the risks that occur. However, the availability of this information, especially in the Majene Regency area, is currently still quite minimal. In fact, Majene is one of the areas with a fairly high Disaster Risk Index. This activity embraces the Voluntary Corps (KSR) PMI Majene Headquarters Unit as a partner considering that KSR is one of the organizations engaged in the humanitarian field and actively participates in disaster management. The activities carried out include the use of a disaster education information system by providing training to KSR members in managing the system and creating disaster education content. The results of the activities obtained show an increase in the ability of KSR members in terms of management and content creation using Canva and hopes that the disaster education information system that has been built can continue to be used for the dissemination of disaster information.
Implementasi Hybrid Filtering (Collaborative dan Content Based) untuk Rekomendasi Kedai Kopi: Implementasi Hybrid Filtering (Collaborative dan Content Based) untuk Rekomendasi Kedai Kopi Zhafirah, Inas; Wajidi, Farid; Asnan Cirua, A.Amirul
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 9 No. 2 (2026): Volume IX - Nomor 2 - Februari 2026
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v9i2.919

Abstract

Perkembangan pesat usaha kedai kopi di Majene menyebabkan konsumen sulit menentukan pilihan kedai kopi yang sesuai dengan preferensi mereka. Kurangnya informasi dan banyaknya pilihan membuat proses pemilihan menjadi tidak efektif. Selain itu, penggunaan Google Maps dan media sosial lainnya sering kali hanya menampilkan kedai yang populer atau memiliki banyak ulasan, sehingga tidak mampu merekomendasikan kedai kopi yang sesuai dengan preferensi personal pengguna, khususnya di daerah dengan keterbatasan interaksi digital seperti Majene. Untuk mengatasi masalah ini, penelitian ini mengembangkan teknologi rekomendasi kedai kopi menggunakan pendekatan hybrid filtering, yaitu penggabungan metode collaborative filtering dan content-based filtering. Data dikumpulkan melalui kuesioner dan wawancara, kemudian dianalisis menggunakan metode adjusted cosine similarity dan weighted sum untuk collaborative filtering, serta TF-IDF dan cosine similarity untuk content based filtering. Hasil dari kedua metode digabungkan menggunakan pembobotan tetap untuk menghasilkan skor rekomendasi akhir. Evaluasi sistem dilakukan dengan metrik Precision@5 pada 10 pengguna yang memenuhi kriteria. Hasilnya, pendekatan hybrid menunjukkan precision tertinggi sebesar 0,92, mengungguli metode tunggal collaborative (0,76) dan content-based filtering (0,82). pendekatan hybrid terbukti meningkatkan kualitas rekomendasi sesuai preferensi pengguna.
Klasifikasi Jenis Rempah-Rempah Menggunakan Metode Algoritma Convolutional Neural Network (CNN) Lukman; Farid Wajidi; Ismaun Rusman
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/kczxhd54

Abstract

Spices play a crucial role in the lives of Indonesian people, where these ingredients are utilized as flavor enhancers, natural dyes, preservatives, and primary components in traditional medicine. Nevertheless, a significant portion of the population still encounters difficulties in distinguishing various types of spices due to morphological similarities between groups, as observed in ginger, turmeric, galangal, lesser galangal, Java ginger, and red ginger. This research is designed to classify these six types of spices using the Convolutional Neural Network (CNN) method. The dataset employed consists of 600 spice images, with each class comprising 100 images, subsequently divided into training data (80%) and testing data (20%). The research process encompasses dataset collection, the pre-processing stage involving resizing images to 46x46 pixels, data division, CNN model training, and performance evaluation through the confusion matrix. The results indicate that the CNN model successfully recognizes the ginger and red ginger classes with high accuracy, whereas in the classes of lesser galangal, turmeric, and Java ginger, classification errors persist due to visual similarities between spices. Overall, the CNN approach proves effective for spice classification, although further enhancements are required in specific classes to achieve more optimal and accurate outcomes.
Analyzing Opinion Polarization on Joko Widodo's Diploma Using Machine Learning Julianti, Julianti; Wajidi, Farid; Musawwir, Musawwir
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.38616

Abstract

The authenticity of former President Joko Widodo's diploma has become a hot topic on the digital space, especially in the comments section of Kompas TV's YouTube channel. The wide diversity of opinions reflects a polarization of public opinion that is worth further analysis. Given the large volume of text data from public comments, manual analysis is ineffective; a technology-based approach is needed to systematically group opinions. Therefore, this study was conducted to analyze public opinion polarization using a machine learning approach. Two classification algorithms, Naive Bayes and Random Forest, were used to distinguish between pro and con public comments on the issue. Data were obtained through an automated collection process (web scraping), followed by text pre-processing and TF-IDF (Term Frequency–Inverse Document Frequency) word weighting. The test results showed that the Random Forest algorithm performed best, with an accuracy of 91%, while Naïve Bayes achieved only 74%. This shows that the Random Forest method is more effective than the Naïve Bayes approach in detecting unstructured text patterns. This study concludes that machine learning can be used effectively to identify trends in public opinion on social media and can serve as a basis for further research using word embedding and deep learning models.
Two-Stage Transfer Learning with EfficientNetB0 for Four-Class Banana Ripeness Classification Anhar Anhar; Farid Wajidi; Chairi Nur Insani
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.34588

Abstract

Manual visual inspection to assess banana ripeness is subjective and unable to meet the scale of industrial needs, while previous studies have used heavy CNN architectures and have not systematically explored the depth of fine-tuning. This study proposes a two-phase transfer learning framework using EfficientNetB0 on a pre-augmented dataset of 13.478 images across four ripeness classes: unripe, ripe, overripe, and rotten. Class imbalance is addressed through class weighting during training. In Phase 1 (Feature Extraction), all base layers are frozen, and the classification head is trained until it achieves a best validation accuracy of 98.58%. In Phase 2 (Fine-Tuning), the optimal unfrozen layer depth was determined through systematic ablation across five configurations (5, 10, 15, 20, and 25 layers), with the 25-layer configuration yielding the highest validation accuracy of 98.75%. Evaluation on 562 test images yielded an accuracy of 99.46% and a test loss of 0.0538, with an F1-score of 1.00 for the overripe and ripe classes, and 0.99 for the rotten and unripe classes. The ROC curve confirmed high discriminative capability with an AUC of 1.000 for the overripe and ripe classes, and 0.999 and 0.998 for the unripe and rotten classes. These results demonstrate that the combination of a two-phase strategy, depth ablation, and fine-tuning with class weights yields a robust classification system with potential for application in automated banana sorting using edge devices.
Co-Authors Ahyar, Muh. Amalia Sutriana Andris Bertohir Panginan Anggit Priadmojo Anhar Anhar Arfa Arfa Arfiah, Sitti Arifin, Nurhikma Asmawati S Asnan Cirua, A. Amirul Asnan Cirua, A.Amirul Bazar, Ikbal Chairi Nur Insani Dian Megah Sari Dian Putri Erdiansyah Rahmi Firgiawan, Wawan Fitriani Fitriani Heliawaty Hamrul Heliawaty Hamrul, Heliawaty Hidayat, Fahril Ibrahim, Nurjannah Iin Aisyah Khofifah Indra Irfan AP Ismaun Ismaun Ismaun Ismaun Rusman J.A, Yunita Hadrianti Julianti Julianti, Julianti Lestari, Sri Apriani Puji Lukman M. Imam Quraisy M.Rio Hidayat Maskur, Nurftriani Maulidia, Ulfa Muh. Adrian Muh. Adrian Muhammad Hambal Muhammad Hanafiah Muhammad Nawir Mujahid Mujahid Mukhlis, Jafar Mulawarman, Ade Musawwir, Musawwir mustafa, mukhdar Mustari Mustari Musyirifah, Musyirifah Musyrifah Nabila, Putri Nirwana, Hafsah Nur Aqida Tul Izzah Nur, Nahya Nuramsah Zulkarnaim Nurfaidah . Nurmadinah, Nurmadinah Nurul Islamiati Parenreng, Jumadi M. Putri, Haerya Quraisy, Imam Rahmiyatal Munaja Rasyid, Muh. Rafli Rensa, Erma Reski Idrus Ria, Canda Rifky Gunawan Rifky Gunawan Rusman, Ismaun Sakina, Nur Sari B, Intan Siti Aulia Rachmini Sugiarto Cokrowibowo Sulfayanti Sulfayanti, Sulfayanti Uca, Uca Virda Evi Yanti Deril Widyanti Utami A Wildayani Wildayani Wirawan, I Putu Andreana Yusrialdi Yusrialdi Zahir Zainuddin Zhafirah, Inas