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All Journal Sistemasi: Jurnal Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi dan Informasi Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Teknologika Jurnal Sains dan Teknologi
Teguh Iman Hermanto
Program Studi Teknik Informatika, Sekolah Tinggi Teknologi Wastukancana Purwakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary Other

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PENJADWALAN MATA KULIAH MENGGUNAKAN ALGORITMA GENETIKA DI STT WASTUKANCANA PURWAKARTA Umar Hasan; Teguh Iman Hermanto; M. Rafi Muttaqin
Jurnal Teknologi dan Informasi (JATI) Vol 8 No 2 (2018): Jurnal Teknologi dan Informasi (JATI)
Publisher : Program Studi Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.921 KB) | DOI: 10.34010/jati.v8i2.1040

Abstract

Generating a study at STT Wastukancana Purwakarta is strongly important to get an effective way to study. As the allocation of rooms to the schedule made is still manual, it is possible there are some room service clashes. To solve that problem, a study scheduling using computerization is needed in order to make no clash of room service. The use of genetic algorithm to solve the problem is able to get the optimal solution in generating the schedule with chromosome representative as an integer from each of data primary key, the beginning of population initialization, the selection with rank-selection method, two-points crossover method, and mutation. From the test, the result points out that the optimal schedule with 416 pengampu data is generated when a number of population are 30, a number of generations are 150, the crossover probability value (Pc) is 0.4, and the mutation probability value (Pm) is 0.37.
Penerapan K-Means Clustering dan Cross-Industry Standard Process For Data Mining (CRISP-DM) untuk Mengelompokan Penjualan Kue Muhammad Rafi Muttaqin; Teguh Iman Hermanto; Muhamad Agus Sunandar
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 19, No 1 (2022): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v19i1.3976

Abstract

Cake is a food that doesn’t have long durability. This will cause the cake producer to suffer a losses if the product is not sold out at the expiration date. With the availability of cake sales data, the sales potential will be clustered according to the date of sale using K-Means method. The data mining process used in this study is Cross-Industry Standard Process for Data Mining (CRISP-DM). The results obtained are the formation of agroup of cake sales that man consumers buy on each date. This grouping is divided into three, namely low, medium, and high sales. This will help producers to prepare their products more effectively and efficiently so as to reduce wasteful production. If the cake is in the low sales group, the number of cake products is small. On the contrary, if there is a cake that goes into high sales group, then the producer will produce the cake in large quantities.
RANCANG BANGUN TEMPAT SAMPAH PINTAR (SMARTBIN) Minarto Minarto; Lise Sri Andar Muni; Candra Dewi Lestari; Teguh Iman Hermanto
Jurnal Teknologika Vol 11 No 2 (2021): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.738 KB) | DOI: 10.51132/teknologika.v11i2.137

Abstract

Semakin meningkatnya jumlah penduduk di suatu wilayah, maka semakin banyak pula sampah yang akan dihasilkan. Jika kita mendengar kotak sampah yang penuh dan didiamkan maka pasti terlintas dibenak kita masalah berupa sekumpulan dari berbagai macam benda yang telah di buang dan sejenisnya yang akan menimbulkan bau busuk dan berbagai macam penyakit seperti gatal-gatal, diare, flu, DBD, dan lain-lain Masalah sampah bukanlah hal yang baru bagi kota-kota besar. Dari permasalahan tersebut penulis melakukan penelitian untuk dapat merancang dan membangun tempat sampah pintar (Smartbin) dengan notifikasi menggunakan metode pengembangan prototype. Adapun beberapa komponen yang digunakan yaitu NodeMCU, motor servo, sensor ultrasonic, modul GPS, speaker, modul mp3 dan lain-lain. Hasil dari penelitian ini adalah perangkat tempat sampah pintar (Smartbin) dengan notifikasi yang dapat memudahkan dalam membuang sampah, dimana tempat sampah akan terbuka secara otomatis ketika sampah akan dibuang. Selain itu juga memudahkan pihak dinas lingkungan hidup dalam mendapatkan informasi ketika tempat sampah sudah terisi penuh yang akan mengirimkan notifikasi melalui telegram. Kata kunci: Tempat Sampah, Prototype, NodeMCU, Motor Servo, Ultrasonic
Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes Yuni Nurtikasari; Syariful Alam; Teguh Iman Hermanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 1 No. 4 (2022): Agustus 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v1i4.770

Abstract

Film is one of the most interesting topics to talk about. When someone writes an opinion on a film, all the elements in the film will be written down. Film opinion data in this study were taken from film comments written on twitter. The number of opinions written on Twitter requires classification according to the sentiments they have so that it is easy to get the tendency of the opinion towards the film whether it tends to have a positive, negative or neutral opinion. Recently, the spotlight on twitter media in Indonesia is a film with the title Horror-Ngeri Sedap. Ngeri-Ngeri Sedap is a 2022 Indonesian comedy-drama film directed and written by Bene Dion Rajagukguk. The film is set in the Batak Tribe, starring Arswendy Beningswara Nasution, Tika Panggabean, Boris Bokir Manullang, Gita Bhebhita Butar-butar, Lolox, and Indra Jegel. This causes differences in the views and opinions of twitter users towards the Horror Sedap film. So it is necessary to have a sentiment classification for the opinion. The use of the Naïve Bayes Algorithm was chosen in the analysis because it has the highest probability or opportunity value for data classification. Labeling on Twitter data is done manually by giving positive, negative, neutral sentiments to the raw dataset in Microsoft excel then the data enters the preprocessing transformation, tokenization and filtering stages. The tf-idf weighting is carried out when the data is complete in the transformation process, tf-idf is used to determine the number of occurrences of words, then the data classification is carried out using the Naïve Bayes Algorithm. confusion matrix testing is done after the data classification is complete using the orange tools. Based on the test results with the confusion matrix with orange tools, the average accuracy value is 0.65% and the precision value is 0.67%, and recall is 0.65%, and the percentage is neutral 0.83% in its classification. This proves that the public sentiment on the twitter platform towards the case of the film Horror Sedap is neutral and the Naïve Bayes Algorithm is considered reliable and valid in data processing.
Development of Hybrid K-Means DBSCAN Algorithm for Optimization of Landslide-Prone Area Clusters based on Web-GIS Dede Irmayanti; Teguh Iman Hermanto
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5671

Abstract

Landslides represent one of the major geological hazards in West Java Province, posing serious impacts on social life, economic activities, and public infrastructure. A key challenge in landslide mitigation lies in the inaccuracy of spatial and temporal classification of landslide-prone areas, as well as the limitations of single-method approaches in disaster data analysis. This study aims to develop a data-driven classification model for landslide-prone areas using a hybrid clustering approach that combines the K-Means and DBSCAN algorithms. The dataset consists of landslide incident records from 2020 to 2024 and administrative spatial data at the regency/city level. The analysis stages include data integration and normalization, statistical exploration, the application of K-Means clustering as a global segmentation framework, and DBSCAN for identifying local patterns and outliers. Model validation was conducted using internal evaluation metrics, yielding a Silhouette Coefficient of 0.448 and a Davies–Bouldin Index of 0.602, indicating that the hybrid method provides superior performance in terms of cluster compactness and separation. The classification results are visualized through an interactive Web-GIS platform developed using Streamlit and Folium, enabling users to select specific years and classification methods while displaying mitigation strategies based on risk categories. This study concludes that the hybrid clustering approach enhances the accuracy of landslide-prone area classification and makes a significant contribution to the provision of more adaptive and practical spatial information to support mitigation policy decision-making in landslide-vulnerable regions.
Real-Time BISINDO Gesture Detection Using YOLOv8 for a Web-Based Text-to-Speech Prototype Riska Dewi Yuliyanti; M. Rafi Muttaqin; Teguh Iman Hermanto
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13362

Abstract

Communication challenges persist between the deaf and hard-of-hearing community and the general public, largely due to limited awareness and understanding of Indonesian Sign Language (BISINDO) in the broader population. This study develops a real-time web-based system that identifies BISINDO gestures using the YOLOv8 object detection model and converts the recognized gesture sequence into spoken words via a text-to-speech function. The study is based on the CRISP-ML(Q) framework, which includes stages such as understanding the data, preparing the data, building models, assessing their performance, implementing them in real-world applications, and continuously tracking their effectiveness. A total of 1,550 images were independently collected using a laptop camera and categorized into 31 classes, including 30 BISINDO gesture classes and 1 class for negative samples. The dataset was split using a stratified method, allocating 80% for training, 10% for validation, and 10% for testing. The YOLOv8 model was trained on Google Colaboratory using a T4 GPU runtime. The evaluation results indicate that the model achieved a precision of 0.979, a recall of 0.976, an mAP50 of 0.993, and an mAP50-95 of 0.839, which demonstrates robust performance in detecting BISINDO gestures. The developed model was incorporated into a web prototype, with FastAPI serving as the backend and HTML, CSS, and JavaScript utilized for the frontend. The system can identify hand gestures using a webcam, show bounding boxes with corresponding labels, compile the detected gestures into simple text sequences, and produce speech from that information. Latency testing revealed an average response time of around 210 milliseconds when the model was deployed locally and approximately 670 milliseconds when deployed online via Hugging Face Spaces. The system still faces challenges in identifying similar-looking gestures and is influenced by factors such as lighting, hand placement, and the availability of hosting resources. These results indicate that YOLOv8s is effective for detecting the 30 static BISINDO gesture classes evaluated in this study within a controlled data collection setting, though further validation is required before the system can be considered suitable for broader real-world assistive communication use.
KLASIFIKASI JENIS PENYAKIT PADA TANAMAN PADI MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK Bagus Kusuma; Teguh Iman Hermanto; Candra Dewi Lestari
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.
Co-Authors Bagus Kusuma Candra Dewi Lestari Candra Dewi Lestari Dede Irmayanti Lise Sri Andar Muni M. Rafi Muttaqin M. Rafi Muttaqin Minarto Minarto Muhamad Agus Sunandar Muhammad Rafi Muttaqin Riska Dewi Yuliyanti Syariful Alam Umar Hasan Yuni Nurtikasari