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All Journal Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA Journal of Informatics and Technology JSM (Jurnal SIFO Mikroskil) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL EKONOMI MANAJEMEN AKUNTANSI Jurnal Teknik Informatika (JUTIF) Makara Journal of Technology
Sukmawati Nur Endah
Faculty Of Mathematics And Natural Science, Diponegoro University, Tembalang-Semarang 50239, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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PENERAPAN E-COMMERCE UNTUK MENINGKATKAN DAN MEMPERLUAS PEMASARAN DI UMKM (Studi Kasus di UMKM Pengrajin Tahu Putih dan Telur Asin di Kabupaten Klaten) Sutikno .; Satriyo Adhy; Sukmawati Nur Endah
JURNAL EKONOMI MANAJEMEN AKUNTANSI Vol 23, No 40 (2016)
Publisher : LPPM STIE DHARMAPUTRA SEMARANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (181.01 KB)

Abstract

Abstrak Salah satu potensi industri yang menjadi basis perekonomian di kabupaten Klaten yaitu industri tahu dan telur asin. Berdasarkan data yang diperoleh dari Badan Pusat Statistik kabupaten Klaten tahun 2012 bahwa terdapat 6 kelompok sentra industri tahu dan belum terdapat kelompok sentra industri telur asin.  Pada sisi lain, jumlah penduduk kabupaten Klaten dari tahun ketahun mengalami peningkatan sehingga akan menambah masalah pengangguran baru jika tidak diimbangi dengan menciptakan industri baru atau mengembangkan industri kecil yang sudah ada. UMKM yang mempunyai potensi untuk berkembang di kabupaten Klaten saat ini  yaitu UMKM tahu putih dan telur asin. Permasalahan dari UMKM ini yaitu pemasaran masih dilakukan secara tradisional dengan menggunakan tenaga manusia sehingga untuk berkembang menjadi besar akan mengalami kesulitan. Untuk mengatasi permasalagan tersebut diperlukan teknologi yang dapat meningkatkan dan memperluas jangkauan pemasaran, sehingga berkembang lebih cepat. Salah satu teknologi yang berkembang sangat pesat sekarang yaitu dengan memanfaatkan e-commerce. E-commerce merupakan tipe perdagangan yang memanfaatkan internet dalam melakukan transaksi. Metode yang digunakan dalam pembuatan e-commerce ini yaitu dengan metode Web Engineering Method (WEM) yang terdiri dari akuisisi, fase orientasi, fase identifikasi, fase perancangan, fase realisasi, dan fase implementasi. Manfaat yang diperoleh dengan di terapkannya e-commerce bagi UMKM ini yaitu dapat melakukan pemasaran dengan jangkauan yang lebih luas tanpa terbatas oleh jarak dan waktu, komunikasi antara pengelola UMKM dan konsumen dapat dilakukan dengan internet sehingga lebih cepat dan murah, dan data-data produksi, konsumen, dan keuntungan terekam secara otomatis. Kata Kunci : UMKM, Web Engineering Methode (WEM), e-commerce
Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation Agustien, Indah; Widyanto, Muhammad Rahmat; Endah, Sukmawati Nur; Basaruddin, Tarzan
Makara Journal of Technology Vol. 14, No. 1
Publisher : UI Scholars Hub

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This paper proposes human pose interpretation using particle filter (PF) with Binary Gaussian Weighting and support vector machine (SVM). In the proposed system, particle filter is used to track a human object, then this human object is skeletonized using thinning algorithm and classified using SVM. The classification is to identify human pose, whether it is normal or abnormal behavior. Here particle filter is modified through weight calculation using Gaussian distribution to reduce the computational time. The modified particle filter consists of four main phases. First, particles are generated to predict target’s location. Second, the weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, the weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The modified particle filter could reduce computational time of object tracking since this method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method builds Gaussian distribution and calculates particle’s weight using this distribution. Through an experiment using video data taken in front of the cashier of a convenience store, the proposed method reduced computational time in tracking process until 68.34% in average compared to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method, i.e. 90.3%. Combining particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.
Garbage Image Classification Using Deep Learning: A Performance Comparison of InceptionResNetV2 vs ResNet50 Rismiyati, Rismiyati; Situmeang, Axelliano Rafael; Khadijah, Khadijah; Endah, Sukmawati Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4770

Abstract

Garbage problem is a worldwide problem. Efforts to address garbage problem have been performed in several aspect, including automatic garbage classification to support automatic garbage sortation in small scale. In the field of garbage classification, deep learning has been widely used because of its ability to learn feature and also to classify with high accuracy.  Several promising architectures in deep learning such as ResNet50 and InceptionNet have been used for this classification task. InceptionResNet is introduced to combine the strength of both architectures. This research aims to classify Garbage Classification data set which consist of 15150 images from 12 classes by using InceptionResNetV2 architecture. In addition, experiment by using ResNet-50 is also performed to provide comparison of its performance. During experiment, Hyperparamater tuning was performed, namely the learning rate, dropout rate, and the number of neuron in the dense layer. The results show that InceptionResNetV2 outperform ResNet50 in all scenarios. This architecture is able to achieve highest accuracy of 97.54%.  Even though the classification time is longer for InceptionResNetV2, this finding is able to prove the outstanding performance of InceptionResNetV2 in garbage classification. This study contributes to the field of garbage classification by introducing robust and better model for better classification.
Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments Nisa, Iffa Zainan; Endah, Sukmawati Nur; Sasongko, Priyo Sidik; Kusumaningrum, Retno; Khadijah, Khadijah; Rismiyati, Rismiyati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 5: Oktober 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022954868

Abstract

Sampah merupakan salah satu permasalahan global yang dihadapi seluruh dunia termasuk Indonesia. Apabila tidak dikelola dengan baik, jenis dan volume sampah yang semakin meningkat dapat berdampak buruk pada lingkungan dan kesehatan manusia. Pemilahan sampah merupakan langkah awal dalam melakukan berbagai jenis pengolahan sampah. Pemilahan sampah secara manual tidak mudah dilakukan mengingat jumlahnya yang amat besar, sehingga otomatisasi pemilahan sampah diperlukan. Penelitian ini mengusulkan klasifikasi citra sampah menggunakan Support Vector Machine (SVM) dengan ekstraksi fitur Gray Level Co-Occurrence Matrix (GLCM) dan Color Moments serta mengoptimalkan kinerja terbaik dalam proses klasifikasinya. Dataset TrashNet digunakan untuk mengevaluasi metode yang diusulkan. Beberapa parameter penting yang digunakan dalam penelitian ini adalah orientasi sudut GLCM, parameter C (soft margin) pada SVM, dan parameter ???? pada Radial Basis Kernel (RBF). Pembagian data dilakukan menggunakan 10-Fold Cross Validation. Hasil penelitian menunjukkan bahwa kombinasi fitur GLCM dengan orientasi sudut 45° dan Color Moments memberikan rata-rata akurasi terbaik sebesar 78,87% dengan menggunakan parameter C bernilai 32 dan parameter γ bernilai 4. Hasil pengujian terbaik diperoleh pada fold ke-3 dengan akurasi sebesar 85,43% yang digunakan sebagai skenario pengujian data baru. Pengujian terhadap 30 citra sampah baru menggunakan model terbaik memperoleh akurasi sebesar 70%. AbstractWaste is one of the global problems faced by the whole world, including Indonesia. Improper waste management can harm the environment and interfere with health. Waste management involved several steps in handling waste, the first one being waste sorting. In Indonesia, waste sorting is still performed manually. Manual waste sorting is not easy to do because the waste amount is very large. Therefore, automatic waste detection technology is needed to support more optimal waste sorting. This study proposes waste image classification using Support Vector Machine (SVM) with Gray Level Co-Occurrence Matrix (GLCM) and Color Moments as the features. The TrashNet dataset is used to evaluate the proposed method. In addition, 30 additional waste image outside trashnet is used as testing data. Some of the important parameters that are tuned in this study are the angle orientation of the GLCM, C (soft margin) parameter on the SVM, and ???? parameter on the Radial Base Kernel (RBF). Data splitting is done using 10-Fold Cross Validation. The results showed that the combination of GLCM features with 45° angle orientation and Color Moments gave the best average accuracy of 78.87% using C parameter with a value of 32 and γ parameter with a value of 4. The best test results were obtained in the third fold with an accuracy of 85, 43%. This result is used to test the 30 test image outside the TrashNet dataset, and achieve accuracy of 70%.
Co-Authors Adi Wibowo Afry Rachmat Amalia Rachma Yanti, Amalia Rachma Andika Putra Pratama Annisa Apriliani Arif Lutfiyanto Aris Sugiharto Beta Noranita Beta Noranita Beta Noranita Dewi, Fitriana Prasari Dinar Mutiara KN, Dinar Mutiara Eko Adi Sarwoko Eko Prasetiawan Erlangga Erlangga Fachry Abda El Rahman Frediansah Frediansah Ganang Surya Atmaja Ganis Khufad Arridho Hanifah Awliya Harahap, Sanggam Andreas Harjanto, Dhony Syafe’i Helmie Arif Wibawa Iffa Zainan Nisa Indriyati Indriyati Irfan Pradipta Kabul Kurniawan Khadijah Khadijah Khadijah, Khadijah Kushartantya Kushartantya Ma’arif, Samsul Muhamad, Nida Muhammad Muhammad Muhammad Rahmat Widyanto, Muhammad Rahmat Nisa, Iffa Zainan Nurdin Bahtiar Panji Wisnu Wirawan Perawaty Panjaitan Priyo Sidik Sasongko Priyo Sidik Sasongko Ragil Saputra Raina Stefani Budi Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Rizka Ella Setyani, Rizka Ella Roby Hanintyo Nursio Sakti Saputra, Angga Pradana Satriyo Adhy Siradjuddin, Indah Agustien Situmeang, Axelliano Rafael Suryo Hartanto Sutikno . Sutikno, Sutikno Tarzan Basaruddin, Tarzan Tridarma, Panggih Widiyanti, Emilia Yudo Prasetyo Zulkarnaen, Harits Farras Zulkarnaen, Harits Farras