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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Jurnal Riset Informatika INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Infortech JUSTIAN - Jurnal Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Penelitian dan Pengabdian Masyarakat Jotika Indonesian Community Service Journal of Computer Science (IndoComs) Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Fahmi Julianto
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Library & Information Science Other

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Prototype Aplikasi Edukasi Anak Berbasis Mobile Wahyutama Fitri Hidayat; Yesni Malau; Muhammad Fahmi Julianto
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 3 No. 1 (2022): Mei 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v3i1.1185

Abstract

Teknologi informasi dunia dimana saat ini sedang berkembang dengan pesat telah menyasar berbagai aspek di masyarakat yaitu ekonomi, kebudayaan, seni, politik, dan tak terkecuali dunia pendidikan. Tujuan dari penelitian yag dilakukan yaitu menghasilkan prototype aplikasi yang berberbasiskan mobile application dengan diberikan nama Aplikasi Edukasi Anak. Batasan yang digunakan di penelitian ini yaitu hanya akan dibahas mengenai merancangan aplikasi berdasarkan model prototype. Hasil dari penelitian ini berupa rancangan Aplikasi Edukasi Anak berbasis mobile yang dirancang menggunakan aplikasi Justinmind. Hasil rancangan yang dilakukan pengujian dengan digunakannya metode BlackBox dapat menghasilkan fitur aplikasi prototype yang dirancang dapat berjalan sesuai dengan rancangan dan diterima.
Analisis Sentimen Ulasan Aplikasi Detik.Com di Google Play Store Menggunakan Pendekatan Lexicon-Based dan Machine Learning Talcha Ilham Putri; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1195

Abstract

Ulasan pengguna pada Google Play Store merupakan sumber informasi yang dapat digunakan untuk mengetahui tingkat kepuasan pengguna terhadap suatu aplikasi. Namun, jumlah ulasan yang terus bertambah menyebabkan proses analisis secara manual menjadi kurang efektif. Oleh karena itu, diperlukan metode analisis sentimen untuk mengidentifikasi kecenderungan opini pengguna secara otomatis. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Detik.com menggunakan pendekatan Lexicon-Based serta membandingkan kinerja algoritma Naive Bayes, Decision Tree, Random Forest, dan Support Vector Machine (SVM). Data penelitian diperoleh dari Google Play Store melalui proses web scraping. Tahapan penelitian meliputi preprocessing data, pelabelan sentimen menggunakan pendekatan Lexicon-Based, ekstraksi fitur menggunakan TF-IDF, pembagian data latih dan data uji, proses klasifikasi menggunakan algoritma machine learning, serta evaluasi model menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pelabelan sentimen menunjukkan bahwa dari 1.000 ulasan yang dianalisis, sebanyak 591 ulasan (59,10%) termasuk sentimen positif dan 409 ulasan (40,90%) termasuk sentimen negatif. Berdasarkan hasil pengujian, algoritma Decision Tree memperoleh performa terbaik dengan nilai akurasi sebesar 79,6%, precision sebesar 80,1%, recall sebesar 79,6%, dan F1-score sebesar 79,7%. Sementara itu, Random Forest memperoleh akurasi sebesar 77,6%, SVM sebesar 74,5%, dan Naive Bayes sebesar 71,9%. Hasil penelitian menunjukkan bahwa pendekatan Lexicon-Based yang dikombinasikan dengan algoritma machine learning mampu digunakan untuk menganalisis sentimen ulasan pengguna aplikasi Detik.com secara efektif, dengan Decision Tree sebagai algoritma yang memberikan kinerja terbaik pada dataset penelitian.
Analisis Sentimen Komentar Trailer Film Menggunakan Pendekatan Lexicon-Based dan Machine Learning Fariska Adela Nurhidayah; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/fjfa1e80

Abstract

Abstrak – Seiring berkembangnya media sosial, YouTube telah menjadi salah satu tempat utama di mana orang dapat menggunakan kolom komentar untuk berbagi pemikiran mereka tentang film. Penelitian ini menggunakan kombinasi teknik berbasis leksikon dan machine learning untuk memeriksa sentimen penonton mengenai trailer film Andai Ibu Tidak Menikah dengan Ayah. Sejumlah langkah preprocessing, termasuk cleaning, case folding, normalisasi, tokenisasi, stopword removal, dan stemming, diterapkan pada data setelah dikumpulkan melalui scraping komentar YouTube. Leksikon Sentimen Indonesia (InSet Lexicon) digunakan untuk pelabelan sentimen, dan pendekatan TF-IDF digunakan untuk ekstraksi fitur. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk mengoreksi ketidakseimbangan data. Metode Naïve Bayes, Logistic Regression, dan Support Vector Machine (SVM) kemudian digunakan untuk mengklasifikasikan sentimen. Metrik akurasi, presisi, recall, dan F1-score digunakan untuk menilai kinerja model. Dengan akurasi 81.90%, presisi 79.95%, recall 81.90%, dan F1-score 80.87%, hasil ini menunjukkan bahwa algoritma Naïve Bayes berkinerja terbaik. Sementara itu, akurasi SVM dan Logistic Regression masing-masing adalah 66.67% dan 62.86%. Hasil ini menunjukkan bahwa Naïve Bayes mengungguli algoritma lain dalam klasifikasi sentimen dari komentar trailer film. Kata kunci : Analisis Sentimen; Lexicon-Based; Machine Learning; TF-IDF; YouTube;   Abstract - As social media has grown, YouTube has become one of the main places where people may use comment sections to share their thoughts about movies. This study uses a combination of lexicon-based and machine learning techniques to examine viewer sentiment regarding the trailer for the film Andai Ibu Tidak Menikah dengan Ayah. A number of preprocessing steps, including as cleaning, case folding, normalization, tokenization, stopword removal, and stemming, were applied to the data after it was gathered via YouTube comment scraping. The Indonesian Sentiment Lexicon (InSet Lexicon) was used for sentiment labeling, and the TF-IDF approach was used for feature extraction. The Synthetic Minority Oversampling Technique (SMOTE) was used to correct data imbalance. The Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM) methods were then used to classify sentiment. Accuracy, precision, recall, and F1-score metrics were used to assess the model's performance. With an accuracy of 81.90%, precision of 79.95%, recall of 81.90%, and F1-score of 80.87%, the findings show that the Naïve Bayes algorithm performed the best. In the meantime, the accuracy of SVM and Logistic Regression was 66.67% and 62.86%, respectively. These results show that Naïve Bayes outperforms the other algorithms in sentiment classification from movie trailer comments. Keywords: Sentiment Analysis; Lexicon-Based; Machine Learning; TF-IDF; YouTube;
Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Machine Learning Berdasarkan Data Operasional 2025 Cici; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/h4zecc92

Abstract

Abstrak - Produksi kelapa sawit adalah salah satu parameter penting untuk melihat seberapa efisien operasi di perkebunan. Penelitian ini bertujuan untuk meramalkan produksi kelapa sawit (Janjang per Pokok) dengan menggunakan algoritma Machine Learning yang didasarkan pada data operasional tahun 2025. Dataset ini terdiri dari 1.347 catatan operasional yang mencakup 13 variabel fitur. Variabel-variabel ini termasuk luas lahan, produktivitas tenaga kerja, serta fitur turunan seperti Total_Hari kerja pemanen dan Hari Kerja per Hektar. Metode yang digunakan meliputi Regresi Linier, Regresi Pohon Keputusan, dan Regresi Hutan Acak dengan pembagian data sebesar 80% untuk latihan dan 20% untuk pengujian. Hasil evaluasi menunjukkan bahwa Random Forest Regressor memberikan kinerja terbaik dengan nilai R² mencapai 0,9393, Root Mean Square Error (RMSE) sebesar 0,0728, dan Mean Absolute Error (MAE) sebesar 0,0345. Kinerja ini jauh lebih baik dibandingkan dengan Decision Tree (R² 0,8372) dan Linear Regression (R² 0,7966). Analisis pentingnya fitur menunjukkan bahwa variabel Jumlah janjang/tandan dan Jumlah pohon sawit memberikan kontribusi paling besar untuk prediksi. kebaruan penelitian ini ada pada pengembangan fitur operasional khusus untuk perkebunan dan penggunaan pembelajaran ensemble pada data produksi nyata tahun 2025. Model ini diharapkan bisa menjadi alat yang membantu dalam pengambilan keputusan untuk mengoptimalkan penggunaan sumber daya saat panen. Kata kunci : Prediksi; Produksi Minyak Sawit; Pembelajaran Mesin; Hutan Acak; Data Operasional;   Abstract - Palm oil production is one of the important parameters to see how efficient the operation in the plantation. This study aims to forecast palm oil production (Fruits per Plant) using Machine Learning algorithms based on operational data in 2025. This dataset consists of 1,347 operational records that include 13 variable features. These variables include land area, labor productivity, as well as derived features such as Total_Harvester_Working_Days and Working_Days per Hectare. The methods used include Linear Regression, Decision Tree Regression, and Random Forest Regression with a data division of 80% for training and 20% for testing. The evaluation results show that Random Forest Regressor provides the best performance with an R² value reaching 0.9393, Root Mean Square Error (RMSE) of 0.0728, and Mean Absolute Error (MAE) of 0.0345. This performance is significantly better than Decision Tree (R² 0.8372) and Linear Regression (R² 0.7966). Feature importance analysis shows that the variables Number of bunches/stems and Number of oil palm trees provide the greatest contribution to the prediction. The novelty of this research lies in the development of operational features specifically for plantations and the use of ensemble learning on real production data for 2025. This model is expected to be a tool that helps in decision-making to optimize resource use during harvest. Keywords: Prediction; Palm Oil Production; Machine Learning; Random Forest; Operational Data;
Analisis Sentimen Ulasan Berbasis Lexicon Menggunakan Pembobotan Tf-IDF dan Algoritma Machine Learning Meltiana; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/gzkd8504

Abstract

Abstrak - Warung Kopi Asiang merupakan salah satu objek dalam penelitian ini, di mana ulasan pengguna yang terdapat di Google Maps dengan menggunakan kombinasi pendekatan berbasis Lexicon, pembobotan TF-IDF, dan algoritma machine learning. Sebanyak 2.358 ulasan dikumpulkan melalui teknik web scraping menggunakan Instant Data Scraper versi 1.4.1.Proses preprocessing diterapkan pada seluruh data,  mencakup tahapan case folding, cleaning, normalisasi, tokenization, stopword removal, dan stemming.Pelabelan sentimen dilakukan menggunakan metode berbasis lexicon, menghasilkan tiga kategori positif sebanyak 1. 289 ulasan (58,62%), negatif sebanyak 632 ulasan (28,74%), dan netral sebanyak 278 ulasan (12,64%). Data yang telah dilabeli kemudian dikonversi ke dalam bentuk numerik menggunakan TF-IDF dan diklasifikasikan dengan tiga algoritma yakni Support Vector Machine (SVM), Decision Tree, dan Logistic Regression. Optimasi  model dilakukan melalui hyperparameter tuning menggunakan GridSearchCV. Dari hasil evaluasi  Support Vector Machine (SVM) dan Logistic Regression sama-sama mencapai akurasi tertinggi sebesar 80%, di mana pada SVM akurasi optimal tersebut sudah tercapai sejak model dasar (default).Decision Tree menghasilkan akurasi sebesar 71%. Temuan ini membuktikan bahwa integrasi metode Lexicon, TF-IDF, dan machine learning bekerja secara efektif dalam menganalisis sentimen ulasan di Google Maps. Kata kunci : Analisis sentimen; Google Maps; Lexicon; Machine Learning;   Abstract - Warung Kopi Asiang is one of the subjects of this study, in which user reviews found on Google Maps were analysed using a combination of a lexicon-based approach, TF-IDF weighting, and machine learning algorithms. A total of 2,358 reviews were collected via web scraping using Instant Data Scraper version 1.4.1. Preprocessing was applied to all data, covering the stages of case folding, cleaning, normalisation, tokenisation, stopword removal, and stemming; sentiment labelling was performed using a lexicon-based method, resulting in three categories: 1, 289 reviews (58.62%), 632 negative reviews (28.74%), and 278 neutral reviews (12.64%). The labelled data was then converted into numerical form using TF-IDF and classified using three algorithms: Support Vector Machine (SVM), Decision Tree, and Logistic Regression. Model optimisation was carried out via hyperparameter tuning using GridSearchCV. The evaluation results showed that both Support Vector Machine (SVM) and Logistic Regression achieved the highest accuracy of 80%, with SVM having reached this optimal accuracy from the base (default) model. Decision Tree achieved an accuracy of 71%. These findings demonstrate that the integration of the Lexicon method, TF-IDF, and machine learning works effectively in analysing sentiment in Google Maps reviews Keywords: Sentiment analysis; Google Maps; Lexicon; Machine Learning;
Perbandingan Algoritma Machine Learning Berbasis TF-IDF dan SMOTE untuk Analisis Sentimen Ulasan Aming Coffee Emi Wulandari; Riski Annisa; Muhammad Fahmi Julianto
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/hk03ka40

Abstract

Abstrak - Ulasan pelanggan pada Google Maps dapat dimanfaatkan untuk mengetahui persepsi pelanggan terhadap kualitas produk dan layanan suatu perusahaan. Namun, banyaknya jumlah ulasan dan data yang tidak terstruktur menyebabkan proses analisis secara manual menjadi kurang efektif. Penelitian ini bertujuan untuk melakukan analisis sentimen pada ulasan pelanggan Aming Coffee menggunakan metode pembobotan Term Frequency–Inverse Document Frequency (TF-IDF) dan algoritma machine learning. Data penelitian diperoleh melalui proses scraping sebanyak 4.000 ulasan pelanggan dari Google Maps Aming Coffee. Tahapan penelitian meliputi preprocessing data yang terdiri atas cleaning, case folding, tokenisasi, normalisasi, stopword removal, dan stemming, kemudian dilakukan pembobotan TF-IDF, pembagian data, klasifikasi menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Decision Tree C4.5, serta penerapan Synthetic Minority Over-sampling Technique (SMOTE) pada data latih untuk menangani ketidakseimbangan kelas. Evaluasi model dilakukan menggunakan accuracy, precision, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa model tanpa SMOTE memperoleh akurasi tertinggi, yaitu Naïve Bayes sebesar 90,63%, diikuti SVM sebesar 90,50%, dan C4.5 sebesar 88,25%. Setelah penerapan SMOTE, akurasi model mengalami penurunan, namun nilai precision meningkat sehingga model menjadi lebih mampu memperhatikan kelas sentimen minoritas. Hasil Exploratory Data Analysis (EDA) menunjukkan bahwa sentimen positif mendominasi ulasan pelanggan Aming Coffee dengan kata yang paling sering muncul antara lain “kopi”, “coffee”, “enak”, “mantap”, dan “ramai”. Penelitian ini menunjukkan bahwa kombinasi TF-IDF, machine learning, dan SMOTE dapat digunakan untuk analisis sentimen ulasan pelanggan serta memberikan informasi yang bermanfaat dalam evaluasi kualitas layanan dan pengambilan keputusan bisnis. Kata kunci : Analisis Sentimen; TF-IDF; Naïve Bayes; Support Vector Machine; Decision Tree C4.5; SMOTE;   Abstract - Customer reviews on Google Maps can be utilized to understand customer perceptions of a company's products and services. However, the large volume of unstructured reviews makes manual analysis less effective. This study aims to perform sentiment analysis on Aming Coffee customer reviews using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting method and machine learning algorithms. Research data were collected through scraping 4,000 customer reviews from Google Maps. The research stages included data preprocessing consisting of cleaning, case folding, tokenization, normalization, stopword removal, and stemming, followed by TF-IDF weighting, data splitting, sentiment classification using Naïve Bayes, Support Vector Machine (SVM), and Decision Tree C4.5 algorithms, as well as the implementation of Synthetic Minority Over-sampling Technique (SMOTE) on training data to address class imbalance. Model evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix. The results showed that models without SMOTE achieved the highest accuracy, with Naïve Bayes reaching 90.63%, followed by SVM at 90.50% and C4.5 at 88.25%. After applying SMOTE, model accuracy decreased, while precision increased, indicating improved attention to minority sentiment classes. Exploratory Data Analysis (EDA) results revealed that positive sentiment dominated Aming Coffee customer reviews, with frequently occurring words including “kopi,” “coffee,” “enak,” “mantap,” and “ramai.” This study demonstrates that the combination of TF-IDF, machine learning algorithms, and SMOTE can be effectively applied to sentiment analysis and provide useful insights for service quality evaluation and business decision-making. Keywords: Sentiment Analysis; TF-IDF; Naïve Bayes; Support Vector Machine; Decision Tree C4.5; SMOTE;
Implementasi Enterprise Resource Planning Berbasis Odoo Pada Toko Terbis Siti Nurdiani Siti; Muhammad Ifan Rifani Ihsan; Muhammad Fahmi Julianto
Jurnal Sistem Informasi Akuntansi Vol 6 No 1 (2025): Periode Maret 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v6i1.8711

Abstract

Bagi usaha seperti Toko Terbis, pengelolaan operasional bisnis sehari-hari menjadi tantangan yang signifikan. Tantangan ini meliputi pengelolaan stok yang tidak terstruktur, pencatatan keuangan yang rawan kesalahan, serta pengelolaan pesanan yang sering memakan waktu. Proses manual yang selama ini diterapkan sering kali mengakibatkan ketidakakuratan data, penurunan produktivitas, dan kurang optimalnya penggunaan sumber daya. Hal ini tidak hanya berdampak pada efisiensi internal tetapi juga pada kualitas layanan yang diberikan kepada pelanggan. Penerapan sistem ERP berbasis Odoo menawarkan solusi yang relevan untuk mengatasi tantangan tersebut. Odoo adalah platform ERP bersifat open source yang dirancang untuk memberikan fleksibilitas dan kemudahan dalam mengelola berbagai aspek operasional bisnis.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol. 3 No. 4 (2021): September 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i4.97

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
PEMODELAN PREDIKSI TSUNAMI DENGAN MACHINE LEARNING MENGGUNAKAN PYCARET PADA DATA HISTORIS GEMPA Muhammad Iqbal; Siti Nurdiani; Lisnawanty Lisnawanty; Muhammad Fahmi Julianto
Jurnal Informatika Vol 10 No 1 (2026): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v10i1.15571

Abstract

AbstractIndonesia faces high tsunami risk due to its position on the Pacific Ring of Fire. This study analyzes machine learning implementation using PyCaret AutoML framework for tsunami prediction based on earthquake parameters. The dataset consists of 782 earthquake records with 13 features. Methodology includes automated preprocessing with outlier removal (16.88%), 80:20 train-test split, 10-fold cross-validation, and comprehensive evaluation. Results show XGBoost achieved best performance (93.95% accuracy, 97.19% AUC, 90.89% F1-score), LightGBM highest AUC (97.35%), Random Forest highest recall (93.11%), and SVM lowest performance (75.81% accuracy). Detailed analysis of PyCaret's automated workflow validates ensemble boosting superiority for tsunami early warning systems in Indonesia.Keywords: tsunami, machine learning, PyCaret, XGBoost, early warningAbstrakIndonesia menghadapi risiko tsunami tinggi karena posisinya di jalur Cincin Api Pasifik. Penelitian ini menganalisis implementasi machine learning menggunakan framework PyCaret AutoML untuk prediksi tsunami berdasarkan parameter gempa bumi. Dataset terdiri dari 782 rekaman gempa dengan 13 fitur. Metodologi mencakup preprocessing otomatis dengan penghapusan outlier (16,88%), pembagian data 80:20, cross-validation 10-fold, dan evaluasi komprehensif. Hasil menunjukkan XGBoost mencapai performa terbaik (akurasi 93,95%, AUC 97,19%, F1-score 90,89%), LightGBM AUC tertinggi (97,35%), Random Forest recall tertinggi (93,11%), dan SVM performa terendah (akurasi 75,81%). Analisis detail workflow otomatis PyCaret memvalidasi keunggulan ensemble boosting untuk sistem peringatan dini tsunami di Indonesia.Kata Kunci: tsunami, machine learning, PyCaret, XGBoost, peringatan dini 
PERBANDINGAN PENERAPAN ALGORITMA DEEP LEARNING DALAM PREDIKSI HARGA EMAS Muhammad Fahmi Julianto; Muhammad Iqbal; Wahyutama Fitri Hidayat; Yesni Malau
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5559

Abstract

Digital investment is trending because advancements in information technology make access easy through smartphones. Various digital investment instruments attract much interest from the public. Post COVID-19 pandemic, the economic impact of the pandemic is still felt until the end of 2022, requiring people to be smart in managing their finances. Gold investment is considered profitable due to its high value and tendency to increase, unlike the fluctuating stocks. Although easily accessible, investments carry risks, so investors must have sufficient knowledge to maximize profits. This research aims to predict gold prices using several deep learning models, namely Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The dataset used was taken from the Kaggle website, which includes historical gold price data. In this research, various deep learning models were applied and evaluated to determine the best model for predicting gold prices. The results show that the CNN model with Adam optimization and Mean Squared Error (MSE) loss function provides the best performance. The CNN model achieved the lowest Mean Absolute Error (MAE) of 0.004848717761305338, the lowest MSE of 4.3451079619612133, and the lowest Root Mean Squared Error (RMSE) of 0.006591743291392053. These results indicate that the CNN model is more effective in predicting gold prices compared to the ANN, RNN, and LSTM models on the used dataset.
Co-Authors Ahmad Setiadi Ali Mustopa, Ali Cici Destiana Putri Desyandri Desyandri Emi Wulandari Eri Bayu Pratama Fariska Adela Nurhidayah Febriola Fintri Indriyani Lisnawanty Meltiana Muhammad Ifan Rifani Ihsan Muhammad Iqbal Rabiatus Sa’adah Ramliadi Rangga Pebrianto Reza Maulana Ridan Nurfalah Riski Annisa Sella Septri Andini Siti Nurdiani Siti Nurdiani Siti Talcha Ilham Putri Tri Rivanie Umi Khultsum Wahyutama Fitri Hidayat Wawan Nugroho Windu Gata Yesni Malau Zetiyani