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All Journal Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika JTT (Jurnal Teknologi Terpadu) JIKO (Jurnal Informatika dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Terpadu Jusikom: Jurnal Sistem Informasi Ilmu Komputer JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) SmartComp
Hermanto, Teguh Iman
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health

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Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka Undamayanti, Erina; Hermanto, Teguh Iman; Kaniawulan, Ismi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.502

Abstract

During the MBKM program running at several universities in Indonesia, several problems occurred, namely the implementation of the curriculum that did not have a reference, the disbursement of pocket money given was not on schedule, the policies of each partner were different, and the existence of the covid-19 pandemic. The way to find out public opinion or opinion about the MBKM program is to summarize public opinion on Twitter social media. This study aims to analyze the results of the classification of twitter users opinions on the MBKM program in Indonesia through sentiment analysis using the Naive Bayes method based on Particle Swarm Optimization. The research metodology carried out in this study was through the stages of data crawling, text preprocessing, feature extraction, classification, and evaluation. The data used in this study are 428 data. The results of the research in the form of sentiment analysis obtained are positive sentiments of 61.92%, it can be concluded that the MBKM program can be well received by the Twitter user community, especially students. Although there are some negative sentiments that appear around 38.08%. The results of this study can be used as a reference for the MBKM policy development team, especially the Kemendikbud POKJA team, because this program can provide benefits and experiences for students while the results of this research can be used as evaluation material for the team in the future to be even better
Analisis Sebaran Titik Rawan Bencana dengan K-Means Clustering dalam Penanganan Bencana Hermanto, Teguh Iman; Muhyidin, Yusuf
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.332

Abstract

Puwakata Regency has fertile land, agricultural products and abundant natural resources. However, the area is also vulnerable to disaster risk. Based on the data collected, the disasters that occurred in Puwakata Regency included several categories, namely landslides, droughts, hurricanes and floods. The trend of increasing numbers of disasters requires further investigation to prevent an increase in the number of victims. Given the large amount of data available, this information can be obtained through data mining analysis methods. For natural disaster data, the clustering method in data mining is very useful for grouping disaster data based on the same characteristics, so that it can be used as a basis for classifying future disaster events. The k-means algorithm is a model used to form clusters by measuring how close it is to the data set. Therefore, in terms of the location of the disaster, the type of disaster and its impact on the disaster, it is hoped that this research can use the clustering technique with the k-means algorithm to classify disaster-prone points. The results obtained 3 clusters, namely, the type of drought disaster is cluster 0, the type of landslide is cluster 1, and the type of landslide is cluster 2. After forming three clusters, disaster management strategies are drawn up at each disaster-prone point in the Purwakarta area
PERAMALAN PENJUALAN SAHAM NIKEL MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY (LSTM) Mahbubi, Firhan Abdillah; Hermanto, Teguh Iman; Lestari, Chandra Dewi
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3254

Abstract

Indonesia has the world's largest nickel resources, with production of 1.6 million tons out of a global total of 328 million tons by 2022. In 2020, the Indonesian government imposed a ban on nickel ore exports to increase domestic processing and attract investment. Nickel supply reached 26 billion tons with reserves of 11,887 million tons. Mineral and coal investment in 2021 reached US$35 billion. The government plans 53 smelters until 2024, with 19 operating in 2021. PT Resource Alam Indonesia Tbk is active in the industry and faces fluctuations in nickel stock prices, which create problems, namely uncertainty for investors in making investment decisions due to fluctuations in nickel prices on the world market. So, effective stock price forecasting is needed using time series data analysis. This research uses a deep learning algorithm approach: Long Short Term Memory (LSTM). The research method uses CRISP-DM, which includes business understanding, data understanding, data preparation, model building, model evaluation, and deployment. Experimentation uses Python, and visualization uses the Streamlit Framework. This study uses optimal technical parameters to evaluate the LSTM model's effectiveness in predicting Nickel stock prices at PT Resource Alam Indonesia Tbk. The results showed that the Long Short Term Memory (LSTM) model could predict the sale of Nickel shares at PT. Resource Alam Indonesia Tbk (password: KKGI.JK) well, with an MAE value of 33.15, RMSE value of 48.14, MSE value of 2317.33, and MAPE value of 7.39. The best combination of the parameter combinations tested is with batch size 32, epochs 150, and optimizer Adam. The findings provide valuable insights for investors in making more informative and effective investment decisions.
KLASIFIKASI JENIS PENYAKIT PADA TANAMAN PADI MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK Kusuma, Bagus; Hermanto, Teguh Iman; Lestari, Candra Dewi
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1395

Abstract

Padi adalah tanaman pangan utama di Indonesia dan memiliki peran vital dalam perekonomian serta kehidupan sehari-hari masyarakat. Namun, produksi padi saat ini mengalami penurunan akibat serangan hama dan penyakit. Deteksi dini dan klasifikasi penyakit padi yang akurat sangat penting untuk mengurangi dampak negatif ini. Pada penelitian ini dilakukannpembangunan dan pelatihan model Convolutional Neural Network (CNN) untuk mengenali kondisi kesehatan tanaman padi. Model dilatih dengan dataset citra daun padi dan dioptimasi dengan parameter terbaik yaitu 30 epoch, batch size 45, dan optimizer Lion. Hasil pengujian menunjukkan akurasi 75% untuk data uji dengan loss 59%, dan akurasi 76% untuk data latih dengan loss 61%. Model ini juga berhasil diimplementasikan dalam aplikasi mobile berbasis Android. Penelitian ini diharapkan dapat berkontribusi pada sektor pertanian Indonesia dengan menyediakan alat deteksi penyakit padi yang lebih efisien dan efektif.
NAÏVE BAYES ALGORITHM OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION (PSO) FOR COVID-19 VACCINE SENTIMENT ANALYSIS ON TWITTER Nugraha, Rivan Adi; Hermanto, Teguh Iman; Nugroho, Imam Ma’ruf
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2776

Abstract

The Covid-19 vaccine is a vaccine that is quite popular, because it is the most needed and most discussed vaccine. There are 5 types of vaccines that are very popular including AstraZeneca, Moderna, Pfizer, Sinopharm and Sinovac. Sentiment analysis is a branch of text classification with computational linguistics and natural language processing that refers to a broad field, and text mining has a function to analyze opinions, judgments, sentiments, attitudes, evaluations and emotions of a person regarding an individual, organization, certain topics, services and other activities. This study aims to classify public sentiment towards the type of Covid-19 vaccine on social media Twitter, whether the opinion is positive or negative by using the Naïve Bayes algorithm based on Particle Swarm Optimization (PSO). The conclusion of this study is that the results of testing the Naïve Bayes algorithm with PSO using RapidMiner software are 79.17% accuracy, 87.69% precision, 85.07% recall for AstraZeneca vaccine, 68.82% accuracy, 92.29% precision, 71.72% recall for Moderna vaccine, 67.54% accuracy, precision 77.83%, recall 62.95% for Pfizer vaccine, accuracy 93.33%, precision 91.67%, recall 100.00% for Sinopharm vaccine, and accuracy 74.93%, precision 82.61%, recall 70.90% for Sinovac vaccine. It can be concluded that with the help of optimization PSO, the resulting confusion matrix value is greater and is proven to be more accurate. Keywords : Vaccine; Covid-19; Sentiment Analysis; Naive Bayes; Particle Swarm Optimization.
IMPLEMENTASI ALGORITMA LINEAR REGRESSION UNTUK PREDIKSI HARGA SAHAM PT. ANEKA TAMBANG TBK Hermanto, Teguh Iman; Nugroho, Imam Ma ruf; Sunandar, Muhamad Agus; Totohendarto, Mochamad Hafid
Jurnal Transformatika Vol. 19 No. 2 (2022): January 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i2.4396

Abstract

Stock investments that provide high returns, but the higher the benefits offered, the higher the risk that will be faced in investing, especially if it is not supported by knowledge of analyzing stocks. This study utilizes the Data Mining prediction technique with the Linear Regression algorithm on the shares of PT. Aneka Tambang Tbk or ANTM. The dataset that will be used is downloaded through the Yahoo Finance website in the period January 2016 - March 2021. In this study the analytical method used is SEMMA (Sample, Explore, Modify, Model, Assess). With RapidMiner Studio 9.9 tools. The result of testing the RMSE (Root Mean Squared Error) value is 17.135, MSE (Mean Squared Error) is 293.599 and the MAPE (Mean Absolute Percentage Error) value is 1.87%. Based on the MAPE, the accuracy of the Linear Regression algorithm in predicting the stock price of PT. Aneka Tambang Tbk provides high-accuracy predictions.
Klasifikasi Penyakit Daun Singkong Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur VGG16 Berbasis Android Anggraeni, Annisa Mustika; Hermanto, Teguh Iman; Nugroho, Imam Maruf
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cassava plants play an important role as a national food source. However, their productivity has declined in recent years due to leaf disease. Manual disease identification is often inaccurate and slow. This study aims to develop an automatic classification system based on digital images to detect cassava leaf disease quickly and accurately. The method used is a Convolutional Neural Network (CNN) with a VGG16 architecture. The system was developed following the CRISP-DM approach and uses tools such as Python, Keras, TensorFlow, and TensorFlow Lite for integration into Android. The model was trained to recognize five leaf conditions: brown spots, bacterial blight, green mite, mosaic, and healthy. Testing over 50 epochs showed an accuracy of 96%, with precision, recall, and F1-score ranging from 0.93 to 0.98. This approach is superior to the research by Setyanto and Ariatmanto, which only achieved an accuracy of 72.84%. This system helps farmers perform early diagnosis by taking or uploading photos of leaves, enabling more effective disease control.
Diagnosa Penyakit Jantung Berdasarkan Kondisi Tubuh Dengan Metode Artificial Neural Network Febrianti, Nisa; Hermanto, Teguh Iman; Sunandar, Muhamad Agus
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9348

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian yang dapat memengaruhi individu pada usia produktif maupun lanjut usia. Oleh karena itu, strategi deteksi dini sangat dibutuhkan untuk mengurangi risiko komplikasi serta menekan biaya perawatan medis. Penelitian ini mengembangkan model klasifikasi berbasis Artificial Neural Network (ANN) guna meningkatkan akurasi dalam mendiagnosis penyakit jantung. Data penelitian bersumber dari Heart Disease Dataset Kaggle dengan jumlah 1.025 rekam medis pasien yang memuat 14 parameter klinis, di antaranya jenis nyeri dada, kadar kolesterol, detak jantung maksimum, hingga kadar gula darah puasa. Pendekatan CRISP-DM digunakan untuk mengarahkan tahapan penelitian mulai dari pemahaman data, pemilihan fitur, pelatihan model, evaluasi performa, hingga penerapan pada aplikasi mobile. ANN yang dibangun memiliki dua lapisan tersembunyi, menggunakan algoritma optimisasi Adam, dan dilatih selama 50 epoch. Evaluasi menghasilkan akurasi 79,61%, precision 73,53%, recall 94,34%, serta F1-score 82,64%. Model ini berhasil diimplementasikan pada platform Android sehingga memudahkan prediksi kondisi jantung secara efisien. Penelitian ini diharapkan mendukung kemajuan teknologi kesehatan digital dan dapat ditingkatkan dengan dataset yang lebih luas serta arsitektur model yang lebih kompleks.
Implementasi Metode ANN untuk Klasifikasi Diagnosis Tiroid Berbasis Aplikasi Mobile Rengganis, Mega Dwi; Hermanto, Teguh Iman; Sunandar, Muhamad Agus
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 6, No 04 (2025): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v6i04.14502

Abstract

Kelenjar tiroid memegang peranan penting dalam pengaturan metabolisme melalui produksi hormon yang memengaruhi pertumbuhan, pembentukan protein, serta distribusi oksigen dalam tubuh. Gangguan pada organ ini, seperti gondok dan nodul, merupakan masalah endokrin yang umum terjadi di seluruh dunia. Sayangnya, banyak kasus tidak terdeteksi dini akibat gejala yang tidak khas dan kerap diabaikan. Dengan demikian, dibutuhkan metode deteksi dini yang memiliki tingkat keandalan tinggi. Penelitian ini mengaplikasikan metode Artificial Neural Network (ANN) sebagai pendekatan klasifikasi berbasis machine learning untuk identifikasi gangguan pada kelenjar tiroid. Model ANN yang dibangun kemudian diintegrasikan ke dalam aplikasi Android dengan antarmuka yang ramah pengguna. Evaluasi performa menunjukkan akurasi 97%, precision 99%, recall 97%, dan f1-score 98%, mencerminkan kapabilitas model dalam mengenali pola data yang kompleks secara konsisten. Integrasi sistem ke dalam platform mobile terlaksana dengan lancar, menghasilkan alat pendukung diagnosis awal yang efektif dan mudah dijangkau.
KLASIFIKASI TANAMAN HERBAL UNTUK KESEHATAN KULIT DAN RAMBUT BERDASARKAN CITRA DAUN MENGGUNAKAN ALGORITMA CNN DENGAN ARSITEKTUR INCEPTIONV3 Melati, Winda Ayu; Hermanto, Teguh Iman; Nugroho, Imam Maruf
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2578

Abstract

Tanaman herbal telah dimanfaatkan dalam pengobatan tradisional, terutama untuk perawatan kulit dan rambut. Namun, pengenalan tanaman herbal berdasarkan ciri visual seperti bentuk daun masih dilakukan secara manual dan kurang akurat. Penelitian in bertujuan untuk membuat sistem klasifikasi otomatis sepuluh tanaman herbal berbasis citra daun menggunakan arsitektur Convolusional Neural Network (CNN) InceptionV3. Model dibangun menggunakan pendekatan deep learning dan mengikuti kerangka kerja CRISP-DM. Dataset yang digunakan terdiri dari 1.000 citra daun yang telah diproses melalui augmentasi dan penyesuaiaan ukuran. Model dilatih menggunakan algoritma Adamax dengan learning rate sebesar 0,0001 selama 20 epoch. Hasil pelatihan menunjukkan akurasi tingggi, yaitu 97,50% pada data pelatihan, 99,00% pada validasi, dan 98,00% pada pengujian. Evaluasi juga dilakukan dengan confusion matrix dan classification report untuk menilai performa per kelas. Model terbaik kemudian dikonversi ke format TensoFlow Lite dan diintegrasikan ke dalam aplikasi Android offline. Aplikasi ini memungkinkan pengguna melakukan klasifikasi tanaman hanya dengan gambar daun dan memberikan informasi manfaat tanaman secara langsung. Sistem yang dikembangkan terbukti efektif, akurat, dan dapat digunakan dalam kehidupan sehari-hari.
Co-Authors Anggraeni, Annisa Mustika Azhar, Sarah Afifah Dede Irmayanti Defriani, Meriska Fauzi, Ibnu Fiki Febrianti, Nisa Ghozali, Miftah Al Ismi Kaniawulan, Ismi Khasanah, Afifa Nur Krishnavarty, Ayumas Aura Kusuma, Bagus Lestari, Candra Dewi Lestari, Chandra Dewi Ma'ruf, Imam Mahbubi, Firhan Abdillah Melati, Winda Ayu Muhyidin, Yusuf Ningsih, Tiara Sari Nugraha, Rivan Adi Nugroho, Imam Ma ruf Nugroho, Imam Ma'ruf Nugroho, Imam Maruf Nugroho, Imam Ma’ruf Pangestu, Panji Indra Prabowo, Calleb Bhaskoro Ramadhan, Yudhi Raymond Rengganis, Mega Dwi Resmi, Mochzen Gito Saputri, Merly Sari, Anita Wulan Sitohang, Andrian Herbert Parsaoran Sunandar, Muhamad Agus Syakir, Yusuf Totohendarto, Mochamad Hafid Undamayanti, Erina