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All Journal Dinamik TELKOMNIKA (Telecommunication Computing Electronics and Control) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Hadi, Heru Pramono
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Heru Pramono Hadi; Eko Hari Rachmawanto; Rabei Raad Ali
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3683

Abstract

Coral reefs are a type of marine organism that has beauty and benefits for other sea creatures’ ecosystems. However, despite its beauty and usefulness, coral reefs are vulnerable to damage such as coral bleaching, which can impact other coral reef ecosystems. This research aims to classify digital images of healthy, bleached, and dead coral reefs. This research method is DenseNet-121 and MobileNet is based on Convolutional Neural Networks. This research uses a dataset from 1582 coral reef image data with three main classes: 720 were bleached, 150 were dead, and 712 were healthy. The testing process is carried out using several forms of split datasets, namely 60:10:30, 50:10:40, and 70:10:20. The test results obtained with a data sharing percentage of 60:10:30 show that MobileNet architecture achieved 88.00% accuracy, and DenseNet-121 achieved 91.57% accuracy. Using a data split percentage of 50:10:40, MobileNet achieved 84.51% accuracy, and DenseNet- 121 achieved 90.52% accuracy. Meanwhile, with a data separation percentage of 70:10:20, MobileNet achieved 85.48% accuracy, and DenseNet-121 achieved 92.74% accuracy.
Manajemen Sampah Dalam Meningkatkan Circular Economy Di Desa Kebuman, Kecamatan Banyubiru, Semarang Heru Pramono Hadi; Indra Gamayanto; Edi Faisal; Suhariyanto -; Amiq Fahmi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1743

Abstract

Abstrak Permasalahan sampah sudah menjadi permasalahan dunia, terutama sampah anorganik dan B3 yang tidak dapat diurai secara alami, sementara jumlah produksi sampah terus bertambah seiringdengan pertumbuhan penduduk. Dari data statistik Kabupaten Semarang jumlah sampah yang terangkut mulai tahun 2019 sebanyak : 220 487 M3, tahun 2020 : 247 095 M3 dan tahun 2021 : 280 859 M3, hal ini menunjukkan peningkatan jumlah sampah naik secara liner. Desa KebumenKecamatan Banyubiru Kabupaten Semarang menghadapi permasalahan yang serupa dengan meningkatnya volume sampah rumah tangga berdampak pada lingkungan yang kurang sehat. Meskipun sudah ada bank sampah pada wilayah tersebut namun ada beberapa kendala yangdihadapi yaitu manajemen sampah, reduce, reuse dan recycle atau 3 R belum optimal. Program PKM (Program Kemitraan Masyarakan) Universitas Dian Nuswantor dengan penerapan manajemen sampah yang efektif dan efisien dengan metode FDG (Focus Group Discussion) dan Edukasi dan Pelatihan diharapkan sampah yang terdapat diwilyah tersebut diolah baik sehingga dapat meniminalkan dampak negatif sampah terhadap lingkungan hidup desa Kebumen dan dapatmenciptakan circular ekonomi, sehingga dapat meningkatkan taraf ekonomi masyarakat setempat Kata kunci: Pengelolaan, Sampah, Manajemen, Taraf Hidup, Ekonomi
Perbandingan Metode Peramalan ARIMA dan Single Exponential Smoothing pada Kasus Kejadian Demam Berdarah Dengue di Kota Semarang Fahmi, Amiq; Maurensa, Giacinta; Hadi, Heru Pramono; Hindarto, Aris Nur; Wibowo, Sasono; Sugiarto, Edi
JOINS (Journal of Information System) Vol. 8 No. 2 (2023): Edisi November 2023
Publisher : Program Studi Sistem Informasi, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v8i2.9335

Abstract

Demam berdarah dengue (DBD) merupakan masalah kesehatan yang signifikan di Indonesia, khususnya di Kota Semarang. Setiap tahunnya, terdapat tren peningkatan penderita demam berdarah. Jika pemangku kepentingan tidak melakukan tindakan dan kebijakan preventif, hal ini akan berdampak buruk pada kesehatan dan kesejahteraan masyarakat. Peramalan kasus di masa yang akan datang merupakan salah satu upaya pencegahan dan pengendalian penyakit DBD. Penelitian ini menggunakan teknik peramalan ARIMA dan Single Smoothing Exponential. Data time series yang digunakan adalah bulan Januari sampai dengan Desember 2022 berdasarkan kasus kejadian di tingkat kecamatan Kota Semarang. Hasil percobaan kedua metode tersebut kemudian dibandingkan untuk mencari hasil terbaik dalam memprediksi jumlah kasus DBD di Kota Semarang. Hasil penelitian menunjukkan bahwa metode ARIMA memberikan hasil terbaik, dengan nilai MSE dan MAE yang lebih kecil.
ANALISA FITUR EKSTRAKSI CIRI DAN WARNA DALAM PROSES KLASIFIKASI KEMATANGAN BUAH RAMBUTAN BERBASIS K-NEAREST NEIGHBOR Hadi, Heru Pramono; Rachmawanto, Eko Hari
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 5 No 2 (2022): Jurnal SKANIKA Juli 2022
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1928.311 KB) | DOI: 10.36080/skanika.v5i2.2944

Abstract

Klasifikasi citra buah rambutan leci, lengkeng, pulasan dan rambutan yang merupakan buah dalam stau spesies telah dilakukan. Klasifikasi buah rambutan menggunakan KNN saja atau fitur ekstraksi saja sudah pernah dilakukan. Dalam penelitian ini, proses klasifikasi kematangan buah rambutan dilakukan dengan K-NN berbasis fitur ekstraksi ciri dan warna dengan tujuan untuk meningkatkan akurasi klasifikasi citra. Terpilih ekstraksi ciri GLCM dan ekstraksi ciri warna HSV, dimana masing-maisng mempunyai keunggulan masing-masing. Berdasarkan 100 dataset citra dalam 4 kelas yaitu mentah, setengah matang, matang dan busuk, telah dilakukan percobaan bervariasi menggunakan sudut GLCM dari 00, 450, 900, 1350dan nilai K=1,3,5,7,9,11,13. Akurasi terbaik yang dihasilkan yaitu 97,5% pada K=1 dan 00. Sedangkan yang terendah pada K=13 dan 1350 dengan hasil 62,5%.
Learning Vector Quantization for Robusta and Arabica Coffee Classification Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Hadi, Heru Pramono
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.7343

Abstract

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.
Customer Segmentation Using K-Means Clustering with RFM Method (Case Study : PT. Dewangga Travindo Semarang) Winaryanti, Hida Sekar; Hadi, Heru Pramono; Rachmawanto, Eko Hari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10440

Abstract

PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.
Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency Rohman, M. Hilma Minanur; Alzami, Farrikh; Hadi, Heru Pramono; Arifin, Zaenal; Sukamto, Titien Suhartini; Ashari, Ayu; Yusuf, Moh.
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14311

Abstract

Stunting, characterized by impaired growth and development in children, is one of the most serious public health problems often caused by chronic malnutrition. This study aims to identify patterns among stunting cases through clustering analysis of child health data. The algorithm used in this research uses K-Means. The dataset used in this study uses health data from 599 children in the Sambas Regency area of East Kalimantan Province. This dataset has several features that are quite diverse such as height, weight, age, nutritional intake, socioeconomic status, and others. This research process begins with cleaning the data, as well as looking at the correlation between features. One of the methods used is to conduct a data analysis process using Principal Component Analysis (PCA) which aims to reduce the dimensions of the data. After that, the process of finding the number of clusters using the Elbow method is carried out to determine the optimal number of clusters. This research uses 4 clusters in the process. The clustering results revealed that family structure (main family vs extended family) and parental income levels significantly influence stunting prevalence in the region.
Peningkatan Kesadaran Masyarakat Desa Jatimulyo Dalam Pengelolaan Lingkungan Melalui Implementasi Bank Sampah Hadi, Heru Pramono; Jannah, Safira Baiti; Cahyani, Tiara Indah; Suhariyanto, Suhariyanto; Faisal, Edi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.1934

Abstract

Desa Jatimulyo terletak di Kecamatan Bonang, Kabupaten Demak, Provinsi Jawa Tengah, Indonesia. Berdasarkan data kampung KB BKKBN tahun 2017, jumlah penduduk pada Desa Jatimulyo adalah 3.358 Jiwa, dengan persentase data yang ada terdapat 36,74% masyarakat bekerja dan 63,26% masyarakat tidak bekerja. Tim PPK Ormawa DPM FIK UDINUS berkolaborasi dan bermitra dengan Desa Jatimulyo sebagai kesepakatan dilaksanakannya Program Sapta Literacy Corner (SATERNER), dan salah satunya adalah Program Workshop Literasi EcoLife mengenai pengelolaan barang bekas menjadi produk kerajinan serta implementasi pembentukan bank sampah sebagai sarana kesadaran masyarakat akan pelestarian lingkungan. Dalam pelaksanaan program ini menggunakan metode PAR (Participatory Action Research) dengan beberapa tahapan yaitu observasi, diskusi kelompok, workshop, serta evaluasi pre dan post test. Dari pelaksanaan program ini menunjukkan adanya peningkatan yang signifikan dari segi pengetahuan dan kesadaran peserta akan upaya-upaya dalam menjaga dan melestarikan lingkungan. 
An Ensemble Learning Layer for Wayang Recognition using CNN-based ResNet-50 and LSTM Irawan, Candra; Rachmawanto, Eko Hari; Hadi, Heru Pramono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2053

Abstract

Wayang is commonly used to tell epic stories of Mahabharata and Ramayana, as well as local legends and myths. There are various types of wayang, such as wayang kulit (made of buffalo or goat leather), wayang golek (made of wood), and wayang klithik (combination of leather and wood). Although it indicates cultural richness, such diversity also makes it difficult for the general public to identify the character of wayang they are seeing because each type has unique characteristics and details. Recognizing   wayang characters is a challenging task due to their intricate designs and subtle variations. This research addresses this problem by leveraging machine learning technology, specifically CNN-based classification methods, to accurately identify wayang characters. This study proposed a novel method that integrates ResNet-50 transfer learning with LSTM, enhancing the model's ability to capture both spatial and sequential features of wayang images. The proposed model achieved an impressive accuracy of 97.92%, with precision, recall, and F1-scores all reaching 100%. Despite the extended training time of 188 minutes and 21 seconds, the results demonstrate the model's superior performance. This advancement can significantly aid in the preservation and educational dissemination of Indonesian cultural heritage. Future research can focus on optimizing the training process to reduce the time while maintaining or even improving the accuracy, potentially expanding the model's application scope and effectiveness.
Clustering and Profiling Analysis for FIFA Football Player using K-Means Azzami, Salman Yuris Adila; Hadi, Heru Pramono; Alzami, Farrikh; Irawan, Candra; Nurhindarto, Aris; Sulistyono, MY Teguh
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.7897

Abstract

The selection of football players is a complex process involving talent evaluation based on various performance indicators, combining objective measures with subjective assessments by coaches and scouts. This research aims to improve the football player selection process using the K-Means clustering method based on the attributes of transfer price, performance, body specifications, position, and player ability. The dataset used consists of 17.947 players taken from the FIFA 19 edition of the soFIFA.com platform, which includes complete information such as transfer price, performance, body specifications, position, and player ability. The data was processed using principal component analysis (PCA) to reduce the dimensions, followed by the Elbow Method to determine the optimal number of clusters. The clustering results show the distribution of players based on their on-field roles, such as center back, goalkeeper, striker, and left wing back. The profiling of players from each cluster is identified based on position, body type, dominant foot usage, transfer price, and rating. This research provides useful insights for coaches and scouts in selecting players that suit specific roles in the team using better analysis. The findings also highlight the importance of player clustering for data-driven decision-making, which can optimize team composition and overall performance.
Co-Authors Ali, Rabei Raad Alzami, Farrikh Ashari, Ayu Aufa Nisrina Aulia Azzami, Salman Yuris Adila Cahaya Jatmoko Cahyani, Tiara Indah Candra Irawan Christy Atika Sari Edi Faisal Edi Faisal Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Fahmi Amiq Faisal, Edi Heru Lestiawan Hindarto, Aris Nur Indra Gamayanto Jannah, Safira Baiti Maurensa, Giacinta Moh. Yusuf, Moh. MY. Teguh Sulistyono Nurhindarto, Aris Pristyanti Nawang Putri R. Septiansa Anggoro Rabei Raad Ali Rohman, M. Hilma Minanur Sasono Wibowo Sinaga, Daurat Suhariyanto - Suhariyanto Suhariyanto Sukamto, Titien Sukamto, Titien Suhartini Titien S. Sukamto Titien Sukamto Wellia Shinta Sari Winaryanti, Hida Sekar Zaenal Arifin