Article Per Year (5 Year)
p-Index From 2021 - 2026
3.833
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal Riset Informatika Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sosial dan Abdimas Journal of Information Systems and Technology Research Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Terapan (J-TETA) Journal of Community Service (JCOS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
idhom, Mohammad
Unknown Affiliation
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 31 Documents
Search

 1   2   3   4 
Prediction of Purchase Volume Coffee Shops in Surabaya Using Catboost with Leave-One-Out Cross Validation Nariyana, Calvien Danny; Idhom, Mohammad; Trimono, Trimono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i1.30610

Abstract

Indonesia's coffee consumption grew from 265,000 tons in 2015 to 294,000 tons in 2020. Averaging 2% annual growth with a projected 368,000 tons by 2024. One of the coffee businesses is coffee shops, Coffee shop businesses often struggle to attract customers quickly, risking low purchase volume within their first five years. In their first year, challenges include management, company size, service quality, and customer preferences.  This study adopts a quantitative approach and new solutions to develop a purchase prediction application based on machine learning and strategy to enhance purchase volumes for three coffee shops in Surabaya. It utilizes CatBoost, with LightGBM as a comparison, across multiple coffee shop locations. LOOCV (Leave-One-Out Cross-Validation) is used in this model to address research limitations, such as data overfitting and biases, while enhancing evaluation accuracy. As a result, the study established CatBoost as the superior model for purchase prediction, providing insights and practical applications in business forecasting. The Catboost model achieved an MAE of 0.91 and MAPE of 15%, outperforming LightGBM’s MAE of 1.13 and MAPE of 18%. These results confirmed CatBoost’s effectiveness for the coffee shop industry with good accuracy. This research also contributes to helping coffee shop owners in Surabaya understand market characteristics, such as the most profitable coffee types and high-customer-density locations. Additionally, it aids in optimizing purchase volume to leverage profit by developing new strategies based on prediction result.  In conclusion, CatBoost accurately predicts purchase volume, helping coffee shops identify target markets and refine strategies based on customer preferences.
Exploratory Data Analysis and Machine Learning Algorithms to Classifying Stroke Disease Riyantoko, Prismahardi Aji; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida; Idhom, Mohammad
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.79 KB) | DOI: 10.33005/ijconsist.v2i02.49

Abstract

This paper presents data stroke disease that combine exploratory data analysis and machine learning algorithms. Using exploratory data analysis we can found the patterns, anomaly, give assumptions using statistical and graphical method. Otherwise, machine learning algorithm can classify the dataset using model, and we can compare many model. EDA have showed the result if the age of patient was attacked stroke disease between 25 into 62 years old. Machine learning algorithm have showed the highest are Logistic Regression and Stochastic Gradient Descent around 94,61%. Overall, the model of machine learning can provide the best performed and accuracy.
Implementation Of Docker Container On Local Network By Applying Reverse Proxy Wahanani, Henni Endah; Idhom, Mohammad; Kristiawan, Kiki Yuniar
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3195.526 KB) | DOI: 10.33005/ijconsist.v3i1.59

Abstract

Virtualization is an implementation of a software. Virtualization technology has changed the direction of the computer industrial revolution by reducing capital costs and operating costs. The availability of a virtualization will also increase the availability of higher services and data protection mechanisms. Docker is configured to create multiple containers on a network, each container containing one image. The three containers will be created in one compose where each container is connected to each other for WordPress configuration and two composers will be created. Furthermore, from each compose a reverse proxy configuration is carried out which aims to set a different domain address. Lighten computer performance and can reduce the required storage so as to make hosting more effective and efficient. Containers also provide a security advantage over complete control over management running on separate, isolated containers.
PENERAPAN MODEL HIBRIDA ARIMA-LSTM PADA PREDIKSI INFLASI DI INDONESIA Shaffa Ameera, Divanda; Terza Damaliana, Aviolla; Idhom, Mohammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13371

Abstract

Pengendalian inflasi penting untuk menjaga stabilitas ekonomi dan kesejahteraan masyarakat. Oleh karena itu, diperlukan perencanaan dan pengelolaan inflasi yang baik sebagai kunci untuk menjaga stabilitas ekonomi dan memastikan pertumbuhan yang berkelanjutan. Prediksi inflasi yang tepat memungkinkan pemerintah dan pelaku ekonomi untuk merancang kebijakan dan strategi yang efektif dalam menjaga stabilitas ekonomi. Penelitian ini bertujuan untuk menerapkan model hibrida ARIMA-LSTM untuk memprediksi inflasi di Indonesia. Konsep model hibrida yang sudah lama digunakan dalam literatur time series memungkinkan peneliti untuk memanfaatkan kelebihan dari setiap model dan memprediksi data dengan lebih efektif. Model ARIMA-LSTM adalah model hibrida yang menggabungkan dua metode peramalan, yaitu ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Model ARIMA diterapkan pada komponen tren, sedangkan model LSTM diterapkan pada komponen musiman dan residual. Penelitian ini menggunakan data inflasi Indonesia sejak 1979 hingga 2024. Nilai terbaik yang didapatkan adalah kombinasi ARIMA (1, 1, 1) dengan LSTM yang menggunakan arsitektur sederhana, dengan satu lapisan LSTM yang terdiri dari 50 unit dan fungsi aktivasi ReLU. Dari hasil penggabungan ARIMA dan LSTM, didapatkan hasil evaluasi MAE = 0.27, MSE = 0.14, dan RMSE = 0.37. Hasil ini menunjukkan bahwa model memiliki performa yang baik.
RANCANG BANGUN ON OFF SEPEDA MOTOR MENGGUNAKAN WIFI DENGAN ESP32 Widi Saputro, Tegar; Rahmat, Basuki; Idhom, Mohammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13626

Abstract

Perkembangan teknologi Internet of Things (IoT) telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dalam bidang transportasi, di mana pengendalian kendaraan secara nirkabel menjadi semakin populer. Namun, sistem pengendalian sepeda motor saat ini masih mengandalkan kunci kontak manual, yang memiliki keterbatasan seperti risiko kehilangan kunci atau kerusakan mekanis, serta kurangnya fleksibilitas dalam pengendalian jarak jauh. Penelitian ini bertujuan untuk merancang dan membangun sistem pengendalian on-off sepeda motor menggunakan teknologi WiFi berbasis ESP32, yang dapat menggantikan fungsi kunci kontak manual dengan sistem nirkabel yang diakses melalui smartphone. Metode penelitian meliputi perancangan sistem, implementasi perangkat keras, dan pengujian fungsionalitas. Sistem ini terdiri dari sepeda motor Supra X 110cc tahun 2004, aki motor, step down, ESP32 WROOM 32D, push button untuk mematikan relay saat pengisian bensin, dan relay 3V sebagai saklar pengganti kontak motor. Tegangan dari aki motor 12V diturunkan menjadi 5V menggunakan step down, kemudian dialirkan ke ESP32, push button, dan relay 3V. Hasil pengujian menunjukkan bahwa sistem dapat berfungsi dengan baik, memungkinkan pengendalian on-off sepeda motor melalui smartphone dengan jarak jangkauan hingga 10 meter di lingkungan terbuka. Sistem ini juga dilengkapi dengan push button sebagai fitur keselamatan untuk mematikan relay secara manual saat pengisian bensin, sehingga menghindari risiko kebakaran. Meskipun demikian, terdapat beberapa keterbatasan, seperti jarak jangkauan yang berkurang di lingkungan tertutup dan waktu respons relay yang mencapai 1-2 detik. Secara keseluruhan, sistem ini diharapkan dapat meningkatkan kenyamanan dan keamanan pengguna sepeda motor.
PENERAPAN METODE MEAN SHIFT CLUSTERING UNTUK MENGELOMPOKKAN WILAYAH BERDASARKAN PENGELOLAAN SAMPAH Lidya Musaffak, Awal; Maulida Hindrayani, Kartika; Idhom, Mohammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13777

Abstract

Pengelolaan sampah di Indonesia menjadi tantangan besar dengan meningkatnya timbulan sampah setiap tahun. Data SIPSN 2023 mencatat timbulan sampah harian sebesar 106.145,71 ton dan tahunan mencapai 38.743.185,18 ton. Setiap wilayah memiliki pola pengelolaan sampah yang berbeda, sehingga diperlukan segmentasi untuk memahami variasinya. Penelitian ini menerapkan algoritma Mean Shift Clustering untuk mengelompokkan wilayah berdasarkan data pengurangan dan penanganan sampah di setiap kabupaten dan kota. Dengan bandwidth 1.5, hasil analisis menunjukkan terbentuknya dua klaster dengan nilai Silhouette Score sebesar 0.649. Terdapat dua klaster yang dihasilkan dengan klaster 1 merupakan klaster dengan sampah yang terkelola rendah sedangkan klaster 2 adalah klaster dengan sampah terkelola tinggi. Hasil penelitian ini diharapkan dapat membantu dalam perumusan kebijakan yang lebih tepat sasaran untuk meningkatkan pengelolaan sampah secara efisien dan berkelanjutan di berbagai daerah.
Complex-Valued Neural Network And Fuzzy Inference System For Image Diagnosis Of Rice Leaf Diseases Mutiara Irmadhani; Syaifullah JS, Wahyu; Idhom, Mohammad
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.370

Abstract

Rice serves as a crucial food crop and holds significant importance in Indonesia's agricultural sector. so the health of rice leaves determines the productivity of the crop. Serious problems such as crop failure often occur due to leaf disease attacks caused by pests or unfavorable climatic factors. Controlling these diseases requires proper knowledge so as not to cause negative impacts on the ecosystem due to misdiagnosis. This research develops a Complex-Valued Neural Network (CVNN) and Fuzzy Inference System (FIS) based method to identify the type of disease and determine its severity. CVNN was used to classify leaf images based on detected visual traits, while FIS analyzed the relationship between these traits and disease severity using fuzzy rules constructed from expert data or input. The results show that CVNN provides superior performance compared to CNN, CVNN model with an accuracy of 92%, where all classes produce high and balanced. While the CNN model also provides satisfactory results with an accuracy of 89%, although there is still an imbalance in some classes. The results of the FIS model on the image The severity of the image of rice leaf disease is the most high category in the leaf blast class is the highest of all classes. The combination of CVNN and FIS model proves that this hybrid approach is effective to support diagnosis, so it can help farmers in making early and precise decisions.
PERBANDINGAN ALGORITMA HDBSCAN DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING DALAM MENGELOMPOKKAN DATA KETENAGAKERJAAN YANG OUTLIERS Permadani, Citra Amelia Intan; Damaliana, Aviolla Terza; Idhom, Mohammad
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7237

Abstract

Ketenagakerjaan merupakan indikator penting dalam mendukung pembangunan ekonomi nasional. Namun, distribusi tenaga kerja di Indonesia masih menunjukkan ketimpangan antarprovinsi. Beberapa provinsi memiliki kontribusi ekonomi dan tingkat pekerjaan formal yang tinggi, sementara yang lain tertinggal. Penelitian ini bertujuan mengidentifikasi pola distribusi ketenagakerjaan antarprovinsi dengan menerapkan analisis klaster menggunakan delapan variabel dari data BPS. Mengingat adanya pencilan dalam data, deteksi outlier dilakukan menggunakan metode Local Outlier Factor (LOF) yang mengidentifikasi enam provinsi sebagai outlier yaitu Jawa Barat, Jawa Tengah, Jawa Timur, DKI Jakarta, Banten, dan Sumatera Utara. Selanjutnya, data dianalisis menggunakan dua pendekatan klasterisasi, yaitu Agglomerative Hierarchical Clustering (Single, Complete, Average Linkage, dan Ward) dan HDBSCAN untuk membandingkan ketahanan metode terhadap data outlier. Validasi kualitas klaster dilakukan dengan Silhouette Coefficient. Hasil menunjukkan bahwa metode Single Linkage memiliki nilai koefisien tertinggi, namun kurang konsisten dalam memisahkan outlier. Sebaliknya, HDBSCAN lebih adaptif terhadap data yang mengandung noise dan pencilan dengan Silhouette Coefficient sebesar 0.546. Dengan demikian, HDBSCAN dinilai lebih efektif dalam analisis klasterisasi data ketenagakerjaan yang kompleks, sementara metode AHC lebih unggul dalam membentuk klaster yang jelas jika pencilan dapat ditangani secara terpisah.
Clustering of the Air Pollution Standard Index (ISPU) in the Province of DKI Jakarta Using the CLARANS Algorithm Azzahra, Adelia Ramadhina; Nabila, Nasywa Azzah; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9783

Abstract

Air pollution has become a serious global issue. According to IQAir's 2024 report, DKI Jakarta ranked 10th among cities with the worst air quality worldwide, indicating that air pollution in DKI Jakarta has reached a concerning level. This research uses the CLARANS algorithm to cluster daily air quality in DKI Jakarta based on pollution parameters. CLARANS is chosen due to its advantages in terms of big data processing efficiency, outlier resistance, and medoid search capability. The novelty of this research lies in the application of CLARANS to overcome the limitations of clustering algorithms in previous research. This research comprises several stages, including data understanding, data preprocessing, building the CLARANS model, and evaluation using the silhouette score. The CLARANS clustering result using the most optimal parameter combination and k = 3 demonstrates well-separated cluster boundaries, with an overall average silhouette score across all regions and years of 0.6398. The analysis results indicate that air pollution in DKI Jakarta tends to worsen in 2024. Jakarta Barat and Jakarta Pusat are predominantly affected by PM10, CO, and O₃ pollution, whereas Jakarta Selatan and Jakarta Utara are more influenced by SO₂ and NO₂ pollution. On the other hand, air pollution in East Jakarta shows a balanced dominance from both pollutant categories.
Application of CNN-BiLSTM Algorithm for Ethereum Price Prediction Diash, Hakam Dzakwan; Nathania, Vannesa; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9757

Abstract

The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Co-Authors Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Adelia Ramadhina Bajramaya, Dewa Widya Basuki Rahmat Masdi Siduppa Cahaya Purtri Agustika Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Diash, Hakam Dzakwan Dwi Arman Prasetya Fahrudin, Tresna Maulana Halim, Rahman Nur Harahap, Jasmine Avrile Kaniasari Henni Endah Wahanani Hindrayani, Kartika Maulida Jauharis Saputra, Wahyu Syaifullah JS, Wahyu Syaifullah Khasanah, Ema Isfa'atin Kristiawan, Kiki Yuniar Kurniawati, Dyah Ayu Listyo Lidya Musaffak, Awal Linggasari, Dienna Eries Lisanthoni, Angela Maulana, Hendra Maulida Hindrayani, Kartika Muhammad Rizki Alamsyah Mutiara Irmadhani Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Pamungkas, Syahrul Ardi Panglima, Talitha Fujisai Permadani, Citra Amelia Intan Priananda, Arya Mahardika Putri, Deva Amalia Rahma Raynaldi, Achmad Riyantoko, Prismahardi Aji Rizaldy Pratama, Alfan Ryan Dana, Alvin Saputra, Wahyu Syaifullah Jauharis Shaffa Ameera, Divanda Sugiarti, Nova Putri Dwi Susrama Mas Diyasa, I Gede Syaifullah JS, Wahyu Terza Damaliana, Aviolla Thohir, A. Zaki Thoriqulhaq, Muhammad Trimono, Trimono Wardana, Azel Christian Wardani, Firly Setya Widi Saputro, Tegar Windyadari, Chrysilla Citra