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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) IT (INFORMATIC TECHNIQUE) JOURNAL REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer U-NET Jurnal Teknik Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) BEES: Bulletin of Electrical and Electronics Engineering Journal of Scientech Research and Development Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) JURNAL ILMIAH INFOKOM PUBLIDIMAS CSRID Jurnal UMKM, Manajemen dan Akuntansi (JUMA)
Setiawan, Adil
Department Of Computer Science, Universitas Potensi Utama, Indonesia
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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EXPLAINABLE MACHINE LEARNING UNTUK PREDIKSI HARGA MOBIL BEKAS DAN ANALISIS FAKTOR PENENTU HARGA Dwi Robiul R; M. Al-Adib; Romi Antoni; Diyo Mollana F; Rahmad S; Fauzan Hamdi R; Adil Setiawan
INFOKOM (Informatika & Komputer) Vol 13 No 1 (2025): JURNAL INFOKOM JUNI 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i1.2322

Abstract

This research aims to predict used car prices and analyze the price determinants using an Explainable Machine Learning (XAI) approach. Used car price prediction presents a significant challenge in the automotive market, where pricing is influenced by various complex variables. The methodology involves comparing the performance of two machine learning models: linear regression (LR) and random forest (RF), trained on a dataset comprising 2,059 used car data points and 19 engineered features. The best-performing model is then interpreted using the SHAP (SHapley Additive exPlanations) method to identify the contribution of each feature. The evaluation results demonstrate that the Random Forest (RF) model exhibits superior performance compared to the Linear Regression model. The Random Forest model achieved a coefficient of determination (R2) of 0.819 and a Mean Absolute Error (MAE) of 294,591.0 . This performance is significantly better than the linear regression model, which yielded an R2 of 0.771 and an MAE of 716,221.3. The SHAP interpretive analysis identified the most significant price determinants. In sequential order, the five most dominant factors influencing price prediction are max power, car age, vehicle length (length_num), vehicle width (width_num), and kilometer (mileage). This finding provides transparent and justifiable insights into the key variables underlying price fluctuations in the used car market.
PREDIKSI JUMLAH WISATAWAN MANCANEGARA KE INDONESIA MENGGUNAKAN ALGORITMA LINEAR REGRESSION DAN RANDOM FOREST REGRESSION Adil Setiawan; Susiana Khosasih; Marulak Lasron Siahaan; Khoiri Sutan Hasibuan; Bualazatulo Laia; Satriyo Wibowo
INFOKOM (Informatika & Komputer) Vol 13 No 1 (2025): JURNAL INFOKOM JUNI 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i1.2326

Abstract

Tourism is one of Indonesia’s leading sectors, contributing significantly to the national economy. Forecasting the number of international tourist arrivals is a strategic necessity to support policy planning and the sustainable development of the tourism industry. This study aims to compare the performance of two regression algorithms, Linear Regression and Random Forest Regression, in forecasting international tourist arrivals to Indonesia. The dataset covers the period 2020–2025, obtained from the Central Bureau of Statistics (BPS) with variables that underwent preprocessing such as normalization and handling of missing values. The methodology includes an 80:20 train-test split, tabular regression, and parameter tuning for Random Forest Regression to enhance model performance. Linear Regression was selected as a baseline model, while Random Forest Regression was chosen for its capability to model nonlinear patterns. Model evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. The results show that Linear Regression produced an RMSE of 59,967.668, MAE of 14,837.645, and R² Score of 0.007, indicating low accuracy. In contrast, Random Forest Regression achieved substantially better results with an RMSE of 9,696.530, MAE of 1,193.143, and R² Score of 0.974. These findings confirm that Random Forest Regression provides higher accuracy than Linear Regression, particularly in addressing seasonal patterns and uncertainties caused by global factors. In conclusion, Random Forest Regression can be considered a more reliable forecasting method for predicting international tourist arrivals. The forecasting results can serve as a basis for decision-making in destination capacity planning, foreign exchange revenue estimation, and risk mitigation against global disruptions (pandemics, geopolitical issues, crises), thereby supporting adaptive and sustainable strategies for national tourism development.
EVALUASI DENSENET-201 UNTUK IDENTIFIKASI BIJI KOPI MENGGUNAKAN HYPERPARAMETER GRIDSEARCH Yuke Manza; Lima Hartimar Rambe; Kiki Putri Ani Siregar; Rika Rosnelly; Adil Setiawan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3898

Abstract

Abstract: Coffee is one of the most important commodities in the global agricultural sector. However, the manual sorting process of coffee beans, which is still widely applied in the Small and Medium Industry (IKM) sector, tends to be time-consuming and often results in inconsistent quality assessments. This study aims to classify coffee bean quality using the DenseNet-201 deep learning architecture, optimized with the GridSearch method to obtain the best combination of hyperparameters. The dataset used consists of 450 images of coffee beans divided into two classes: good-quality and defective beans. The model was trained for 20 epochs using a transfer learning approach and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The test results show that the model before optimization achieved an accuracy of only 78.67%, while the model optimized with GridSearch reached a high accuracy of 99.47% with a low loss value. These findings indicate that the application of DenseNet-201 with hyperparameter tuning is capable of producing accurate and stable classification results, and can be relied upon as an automated solution for sorting coffee beans based on their quality. Keywords: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Coffee Bean Classification Abstrak: Kopi merupakan salah satu komoditas penting dalam sektor pertanian global. Namun, proses pemilahan biji kopi secara manual yang masih banyak diterapkan pada sektor Industri Kecil dan Menengah (IKM) cenderung memakan waktu dan menghasilkan penilaian kualitas yang tidak konsisten. Penelitian ini bertujuan untuk mengklasifikasikan kualitas biji kopi menggunakan arsitektur Deep Learning DenseNet-201 yang dioptimalkan dengan metode GridSearch untuk memperoleh kombinasi hyperparameter terbaik. Dataset yang digunakan terdiri dari 450 gambar biji kopi dengan dua kelas: biji kopi bagus dan biji kopi rusak. Model dilatih selama 20 epoch dengan pendekatan transfer learning dan dilakukan evaluasi terhadap performa model menggunakan metrik akurasi, precision, recall, dan f1-score. Hasil pengujian menunjukkan bahwa model sebelum optimasi hanya mencapai akurasi sebesar 78,67%, sedangkan model dengan optimasi GridSearch mampu mencapai akurasi tinggi sebesar 99,47% dan nilai loss yang rendah. Hal ini menunjukkan bahwa penerapan DenseNet-201 dengan tuning hyperparameter mampu menghasilkan klasifikasi yang akurat dan stabil, serta dapat diandalkan sebagai solusi otomatis dalam proses sortasi biji kopi berdasarkan kualitasnya. Kata kunci: Deep Learning, DenseNet201, Hyperparameter, GridSearch, Klasifikasi Biji Kopi
Inovasi Sistem Pemesanan Online Berbasis QR Code dengan Algoritma Reed-Solomon untuk Meningkatkan Keandalan dan Efisiensi Layanan Adil Setiawan; Bob subhan Riza; Nanda Setiawan; Haliza Safira; Nurmi Panjaitan
Publikasi Pengabdian Masyarakat Vol 6 No 1 (2026): PUBLIDIMAS Vol. 6 No. 1 MEI 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.6.1.2026.56-65

Abstract

Transformasi digital pada usaha kuliner mendorong kebutuhan akan sistem pelayanan yang mampu meningkatkan efisiensi dan ketepatan proses pemesanan. OFA COFFEE masih menghadapi proses pemesanan secara langsung dan pencatatan manual yang berpotensi menimbulkan kesalahan serta kurang efisien. Kegiatan ini bertujuan mengimplementasikan sistem pemesanan online berbasis QR Code dengan algoritma Reed-Solomon untuk meningkatkan keandalan akses dan efisiensi layanan. Metode yang digunakan adalah pendekatan partisipatif dan deskriptif-evaluatif melalui tahapan identifikasi kebutuhan, perancangan dan implementasi sistem, penyusunan panduan penggunaan, sosialisasi, simulasi pemesanan, serta pengujian QR Code. Sistem yang dihasilkan menyediakan fitur akses menu melalui QR Code, pemilihan produk, pemesanan, pencatatan transaksi, pemantauan status pesanan, dan laporan administrasi. Algoritma Reed-Solomon diterapkan sebagai mekanisme koreksi kesalahan untuk membantu mempertahankan keterbacaan QR Code ketika sebagian pola mengalami kerusakan. Hasil implementasi menunjukkan bahwa sistem dapat mendukung digitalisasi proses pemesanan di OFA COFFEE melalui alur pelayanan yang lebih terstruktur dan terdokumentasi. Namun, peningkatan efisiensi waktu dan kepuasan pelanggan belum dapat diukur secara kuantitatif karena belum tersedia evaluasi jangka panjang.
Co-Authors Agung RM Alam Agus F Nduru Andreas Parapat Andri Armaginda Siregar Bagus Riski Bill Raj Bob Subhan Riza Bualazatulo Laia Dimas Setiawan Diyo Mollana F Dwi Robiul R Edy Victor Haryanto Edy Victor Haryanto Edy Victor Haryanto Edy Victor Haryanto, Edy Victor Empiter Gea Evri Ekadiansyah Farhan Sardy Abdillah Fauzan Hamdi R Haliza Safira Hts, Dedek Indra Gunawan Iwan Fitrianto Rahmad Jalaluddin Nasution Johan Khoiri Sutan Hasibuan Kiki Putri Ani Siregar Lima Hartimar Rambe M Prasetyo M Suhada WD M. Agung Irnanda M. Al-Adib M. Riyanto Marulak Lasron Siahaan Muhammad Fahmi Rayda Muhammad Hagi Sandria Muhammad Ridwan Rambe Nanda Setiawan Naufal Dhiya Putra Dalimunthe Novi Yanti Nurmi Panjaitan Pane, Rahmadani Pebruarianto Hutabarat Rahmad S Rambe, Muhammad Ridwan Ria Eka Sari Rika Rosnelly Rita Novita Sari Robby Ardyanto Marsudi Romi Antoni Sahat Sonang Sitanggang Sarjon Defit Satriyo Wibowo Soeheri Soeheri Sri Wahyuni Sumijan Susiana Khosasih Yuhandri Yuhandri, Yuhandri Yuke Manza