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All Journal Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) bit-Tech International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pengabdian kepada Masyarakat Multica Science and Technology Indonesian Journal of Networking and Security - IJNS Malcom: Indonesian Journal of Machine Learning and Computer Science Kapas: Kumpulan Artikel Pengabdian Masyarakat SmartComp E-Amal: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Indonesia

Joang Ipmawati

Universitas Nahdlatul Ulama Yogyakarta

Author-ID : 4380228

Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Physics Public Health Social Sciences Other

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Sistem Pendukung Keputusan Penentuan Penerima Kredit Usaha (Studi Kasus: Adira Finance Kediri) Utomo, Yudho Bismo; Ipmawati, Joang
Creative Information Technology Journal Vol 3, No 4 (2016): Agustus - Oktober
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Program pemerintah dalam mengurangi jumlah pengangguran di negara ini adalah dengan jalan memberikan kredit untuk usaha kepada para nasabah calon pengusaha. Dalam hal ini, PT. Adira Finance yang bergerak di bidang perkreditan ikut andil dalam rencana pemerintah tersebut. Metode yang dilakukan PT. Adira Finance saat ini dalam mengambil keputusan penentuan kelayakan penerima kredit masih menggunakan cara manual, sehingga membutuhkan waktu yang lama untuk menentukan nasabah tersebut layak atau tidak menerima kredit. Untuk itu maka perlu dibuatlah aplikasi sistem pendukung keputusan penentuan kelayakan penerima kredit dengan metode Neural Network algoritma backpropagation. Dari hasil uji coba yang telah dilakukan, dengan parameter learning rate sebesar 0.9; hidden layer 10; maksimum epoch 1000 dan target error 10-5 menghasilkan akurasi yang cukup baik yaitu 98%. Aplikasi telah didesain mampu memenuhi kebutuhan pihak Pimpinan PT. Adira Finance dalam menentukan nasabah mana yang layak menerima kredit.Government programs to reduce the number of unemployed in this country is by providing credit to businesses to customers aspiring entrepreneurs. In this case, PT. Adira Finance engaged in credit took part in the government plan. The method is carried PT. Adira Finance today in making a decision on the merits of credit recipients still use manual way, so it takes a long time to determine the customer is feasible or not receive credit. For that it needs to be made to decision support system application credit recipient eligibility determination by the method of propagation Neural Network algorithm. From the results of trials that have been done, the learning rate parameter of 0.9; hidden layer 10; the maximum error epoch 1000 and targets 10-5 generates good accuracy of 98%. The app has been designed to meet the needs PT. Adira Finance Leaders parties in determining which customers are eligible to receive credit.

Komparasi Teknik Klasifikasi Teks Mining Pada Analisis Sentimen Ipmawati, Joang; ., Kusrini; - STMIK AMIKOM Yogyakarta, Emha Taufiq Luthfi
IJNS - Indonesian Journal on Networking and Security Vol 6, No 1 (2017): IJNS Januari 2017
Publisher : APMMI - Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (931.34 KB)

Abstract - Opininion mining also called sentiment analysis is a computational research of opinions, sentiments and emotions that are textually to see opinion on an issue, or to identify the tendency of things in the market. This time public opinion be an important resource in making decisions for a product. Classification algorithm to perform text mining including Support Vector Machine (SVM), NaÃ¯ve Bayessian classification (NBC) and K-Nearest Neighbor (K-NN). These of algorithms will compired to find out a good performance in terms of accuracy for two different datasets that imdb movie reviews and twitter sentiment. The results of the comparison showed SVM obtain good results in accuracy in the data imdb movie reviews 78.55% and on twitter dataset 72%. Similarly, NBC obtained the data accuracy at 78.55% twitter but different data twitter 67.33%. The results of F-Measure SVM movie review show and NBC showed the same results, namely 0.785 and also for the AUC, the results surpass NBC 0.869, SVM get results 0.786 and while KNN obtain the results 0.572. F-Measure to twitter SVM is superior obtaining results of 0.720 and 0.673 NBC obtained results while K-NN 0.545. and for the results of the AUC, as dataset imdb, on twitter this dataset NBC also outperformed SVM and K-NN. AUC to obtain results NBC 0.735, SVM obtain results K-NN 0.658 and 0.618 get results. Keywords : Text Mining, Sentiment Analysis, SVM, NaÃ¯ve Bayessian, K-NN, compare, comparation Abstrak - Opininion mining juga disebut analisis sentimen adalah riset komputasional dari opini, sentimen dan emosi yang diekspresikan secara tekstual dilakukan untuk melihat pendapat terhadap sebuah masalah, atau untuk identifikasi kecenderungan hal di pasar. Saat ini pendapat masyarakat menjadi sumber yang penting dalam pengambilan keputusan akan suatu produk. Algoritma klasifikasi yang dapat melakukan teks mining diantaranya Support Vector Machine (SVM), NaÃ¯ve Bayessian classification (NBC) dan K-Nearest Neighbor (K-NN). Ketiga algoritma ini akan dikomparasi untuk mengetahui performa yang baik dalam hal akurasi untuk dua dataset yang berbeda yaitu imdb review film dan sentimen twitter. Hasil dari komparasi menunjukkan SVM memperoleh hasil yang baik dalam akurasi pada data imdb review film 78,55% dan pada dataset twitter 72%. Sama halnya dengan NBC yang memperoleh akurasi pada data twitter 78.55% tetapi berbeda pada data twitter 67,33%. Hasil F-Measure review film menunjukan SVM dan NBC memperoleh hasil yang sama yaitu 0,785 dan untuk hasil AUC, NBC mengungguli hasil 0,869, SVM memperoleh hasil 0,786 sedangkan KNN memperoleh hasil 0,572. F-Measure untuk twitter SVM lebih unggul memperoleh hasil 0,720 dan NBC memperoleh hasil 0,673 sedangkan K-NN 0,545. Dan untuk hasil AUC, sama seperti dataset imdb, pada dataset twitter ini NBC juga mengungguli SVM dan K-NN. AUC untuk NBC memperoleh hasil 0,735, SVM memperoleh hasil 0,658 dan K-NN memperoleh hasil 0,618. Kata kunci: teks mining, sentimen analisis, SVM, NaÃ¯ve Bayessian, K-NN, komparasi

KNOWLEDGE MANAGEMENT TECHNOLOGY USING SECI AND WIL TO IMPROVE PERFORMANCE OF IN-FORMATION TECHNOLOGY DIVISION IN PANGESTU JAYA LTD. Joang Ipmawati; Yana Hendriana
Multica Science and Technology Vol 1 No 1 (2021): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v1i1.206

Information Technology Division is one of the divisions in Pangestu Jaya Ltd. which engaged in IT Department, where in this division there are several sub-divisions that are interrelated and have the duty and responsibility of each individual to improve services in The company. Turnover is one of the major obstacles in order to still be able to keep the knowledge technology in the company, because there are still many employees with outsourced status who often do turnover in IT division, so difficult to keep the knowledge. therefor need a knowledge management technology which is capable of storing, distributing knowledge in order to renew even while maintaining corporate knowledge. The system design of this research used SECI and WIL (Work Integrated Learning).

Evaluasi Hasil Pengujian Tingkat Clusterisasi Penerapan Metode K-Means Dalam Menentukan Tingkat Penyebaran Covid-19 di Indonesia Elsa Virantika; Kusnawi Kusnawi; Joang Ipmawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4325

Coronavirus Diseases 2019, often known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Indonesia has a large area so that it is easy to contract COVID-19 and the spread of the Covid-19 virus in Indonesia is growing quite rapidly. Based on the region in Indonesia, it can be grouped into parts of the provinces in Indonesia and generate provincial points for the distribution of Covid-19 cases, aiming to create a strategy for handling the spread of COVID-19 in all provinces in Indonesia. The grouping of the level of spread of COVID-19 is carried out using a data mining method, namely the k-means clustering algorithm by grouping data into several clusters based on the similarity of the data. Based on the results of the study, 3 clusters were identified, namely cluster 0 with a low level of distribution of Covid-19, 12 provinces, cluster 1 with a moderate level of distribution of COVID-19, 18 provinces, and cluster 2 with a high level of distribution of COVID-19, 4 categories. province. Based on the results of this study, it is hoped that it can provide information and support the government to make strategic decisions in each cluster to reduce the level of spread of COVID-19 in Indonesia.

Prediction of Student Satisfaction with Academic Services Using The C4.5 Algorithm (Case Study: Yogyakarta Nahdlatul Ulama University) Joang Ipmawati; Adelia Octora Pristisahida; Ahmad Asyhari
Prosiding Seminar Sains Nasional dan Teknologi Vol 12, No 1 (2022): VOL 12, NO 1 (2022): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v12i1.7214

Tujuan: Universitas Nahdlatul Ulama Yogyakarta merupakan perguruan tinggi swasta yang berdiri sejak tahun 2017 dan telah melaksanakan proses pembelajaran. Dengan visi menjadi lembaga keilmuan untuk mendukung terwujudnya masyarakat adil dan demokratis yang berlandaskan Ahlussunnah wal Jama'ah Islam. Untuk mewujudkan visi tersebut maka proses pengabdian akademik merupakan salah satu yang terus ditingkatkan di lingkungan Universitas Nahdlatul Ulama. Tujuan penelitian ini adalah mengukur tingkat kepuasan mahasiswa berdasarkan prediksi dengan menggunakan Algoritma C4.5. Variabel yang digunakan untuk membuat prediksi adalah hasil kuisioner yang kemudian mengklasifikasikan variabel yang meliputi Tangible, Responsiveness, Reliability, Empathy, dan Assurance. Desain / metode / pendekatan: Metode prediksi kepuasan mahasiswa berdasarkan data dari kuesioner, proses selanjutnya menggunakan tahap data mining dengan menggunakan algoritma C4.5.Hasil: Akan diperoleh dan ditemukan pola yang dapat dideskripsikan dalam bentuk pohon keputusan yang akan digunakan untuk memprediksi kepuasan mahasiswa sebagai bahan informasi dan evaluasi untuk menentukan kebijakan dalam pengambilan keputusan guna meningkatkan kualitas layanan akademik.Keaslian / state of the art: Perbedaan penelitian berdasarkan data objek yang mengacu pada hasil kuisioner yang dilakukan di lingkungan UNUYO dan mampu membuat prediksi berdasarkan data dan fakta yaitu data latih dan data uji dengan menggunakan Algoritma C4.5.

Permodelan UI/UX Aplikasi Santri Information Management System (SAIMS) Menggunakan Metode User Centered Design (UCD) Jumanur Rohman; Nur Azmi Ainul Bashir; Joang Ipmawati; Feri Febria Laksana
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 1 (2023): Februari 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Perkembangan teknologi media informasi telah mengubah pencatatan dari manual menjadi komputerisasi, tidak terkecuali pada lembaga pendidikan pondok pesantren (ponpes). Dampak perkembangan teknologi di ponpes, salah satunya adalah manajemen data santri. Mengingat bahwa setiap tahun jumlah santri di ponpes terus bertambah, tentu hal tersebut sangat berpengaruh terhadap keefektifan dalam memanajemen data santri. Ponpes Mifahussalam Yogyakarta merupakan salah satu ponpes yang media informasinya masih dalam tahap pengembangan dan saat ini belum memiliki aplikasi untuk memanajemen data santri. Sampai saat ini belum ada penelitian yang membahas solusi untuk Ponpes Miftahussalam Yogyakarta. Berdasarkan hal tersebut maka diusulkanlah aplikasi bernama Santri Information Management System (SAIMS). SAIMS merupakan aplikasi yang memanajemen data santri di Ponpes Miftashusalam Yogyakarta. Perancangan desain SAIMS menggunakan metode User Centered Design (UCD). Penelitian ini bertujuan untuk mengetahui seberapa besar tingkat kelayakan desain SAIMS. Pengujian tingkat kelayakan menggunakan metode System Usability Scale (SUS). Hasil penelitian didapatkan angka sebesar 76,8. Angka tersebut artinya NPS (Net Promoter Skor) masuk kategori passive atau pengguna merasa puas. Tingkat penerimaan (acceptable) masuk kategori acceptable yaitu desain dapat diterima. Sifat (adjective) masuk kategori good yang artinya desain bersifat baik. Tingkat (grade) masuk kategori B memiliki peringkat baik. Berdasarkan penjabaran yang telah disebutkan menunjukkan bahwa desain SAIMS layak digunakan.

Pemanfaatan Energi Baru Terbarukan Smart Farming System dalam Peningkatan Hasil Pertanian dan Perikanan Restiadi Bayu Taruno; Ilham Unggara; Joang Ipmawati; Yana Hendriana; Nur Azmi Ainul Bashir; Zulkhairi Zulkhairi
Berdikari: Jurnal Inovasi dan Penerapan Ipteks Vol 11, No 1 (2023): February
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/berdikari.v11i1.16972

Pondok Pesantren (Ponpes) Lintang Songo is a business-based boarding school with 27 business units. Those business units include agriculture, plantations, fisheries, and animal husbandry. Alas, the most common problems in business management are irregular fish feeding, unchecked water quality leading to many fish growing slowly or dying, and the high use of PLN electricity for business unit operations. The aim of the Community Service Program (PKM) was to design automatic tools supported by New Renewable Energy (EBT) technology based on the Internet of Things (IoT) in the form of a Solar Home System. Automatic feeding devices and pond water quality sensors were targeted to overcome the aforementioned issues. The method used in the Community Partnership Program (PKM) was research and development (RD) through the design of the Solar Home System, automatic feeding devices, and pond water quality sensors. After testing the fish feed tools, the condition of the fish improved, and no fish died. Monitoring the pH of pond water shows a pH value of 6.5 – 7 so that it can be categorized as normal with a stable water circulation system. Testing of the solar home system showed the effectiveness of using solar panels at position 120 WP in sunny conditions, which indicates that the utilization of supporting equipment for smart farming at the Lintang Songo Islamic Boarding School had been successful.

Implementasi Teknologi EBT Berbasis Panel Surya Pada Produksi Batik Tulis Kebon Indah Guna Mendukung Green Management Irwan Novianto; Rifqi Syarif Nasrulloh; lilis kurniasari; Adelia Octora Pristisahida; Bledug Kusuma Prasaja; Muhamad Nasruddin Manaf; Zulkhairi Zulkhairi; Joang Ipmawati; Rio Ardiansyah; Akhmad Fakhurrozi; Aditya Wahyu Pratama; Diyon Saputro
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 2, No 1 (2023)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v2i1.2026

Paguyuban Batik Tulis Kebon Indah terletak di Desa Kebon, Kecamatan Bayat, Kabupaten Klaten, Provinsi Jawa Tengah, merupakan salah satu sentra batik tulis dengan menggunakan pewarna alam. Paguyuban Batik Tulis Kebon Indah merupakan UKMK yang sudah mengimplementasikan tata kelola Green Management, salah satunya yaitu konsisten dengan pewarna alam yang digunakan. Tetapi paguyuban ini masih menggunakan minyak tanah dan listrik konvensional dari PLN untuk menyalakan kompor malam batik. Oleh karena itu, salah satu upaya yang dilakukan adalah pemasangan panel surya di paguyuban Batik Tulis Kebon Indah. Dalam kegiatan pengabdian masyarakat ini melibatkan dosen dan mahasiswa Universitas Nahdlatul Ulama Yogyakarta dalam pemasangan panel surya yang mengacu pada Green Management. Metode pelaksanaan meliputi tahap survei lapangan. Tahap berikutnya perancangan kemudian tahap pemasangan dan uji coba yang meliputi pemberian beban output pada instalasi panel surya yang telah dibuat. Pengujian beban dengan menggunakan 5 buah kompor listrik malam batik dapat terlaksana dengan baik. Dimana 5 kompor listrik malam batik dapat menyala secara bersamaan mulai dari jam 10 pagi sampai jam 3 sore. Selain itu juga dilakukan sosialisasi kepada ibu-ibu pengrajin batik tulis guna melaksanakan transfer knowledge serta antisipasi jika terjadi kendala dalam pemakaian listrik berbasis sel surya.

Price Prediction Of Basic Material Using ARIMA Forecasting Method Through Open Data Sumedang District Kusnawi Kusnawi; M Andika Fadhil Eka Putra; Joang Ipmawati
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2282

In the era of Industry 4.0, characterized by the abundance of data, there are many opportunities to carry out various data-related processes. One of these is the data forecasting process which has been widely used. By analyzing data, we can make predictions and make decisions automatically. For example, one of the problems that decision-makers, especially in Kabupaten Sumedang, must solve is the changes in the prices of basic commodities that are essential for society's consumption. The prices of these commodities in the market tend to fluctuate in the short or long term. By analyzing the available data, we can predict the direction of changes in the prices of basic commodities in the market. In this study, the ARIMA model is used, which is one of the time series models that can be used to predict the possibility of an increase or decrease in the prices of basic commodities in the market in Kabupaten Sumedang. The ARIMA model uses the previous day's price data as a benchmark to predict the prices of basic commodities in the future. After being analyzed, the results of the model will be in several ARIMA model forms. An efficient ARIMA model will be used to model the prices of basic food commodities. This research produced the three best ARIMA models, namely ARIMA(1-1-1) for broiler chicken meat, ARIMA(0-1-1) for shallots, and ARIMA(0-1-1) for garlic. The accuracy test results percentage error for the best model using MAPE show an average value below 10%. Keywords: Food staples, Forecasting, Time Series, ARIMA, MAPE

Water Quality Monitoring for Smart Farming Using Machine Learning Approach Hendriana, Yana; Taruno, Restiadi Bayu; Zulkhairi, Zulkhairi; Bashir, Nur Azmi Ainul; Ipmawati, Joang; Unggara, Ilham
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7499

Water quality in fish farming environments has been a topic of research investigation for numerous years. While most studies have concentrated on managing water quality in fish ponds, there is a lack of research on implementing these practices on a commercial scale. Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations. In our study, we gathered weekly data from two fish ponds in the Lintangsongo smart farming area over six months. To deal with the limited dataset, we utilized methods for reducing dimensionality, like the pairwise comparison of correlation matrices to eliminate the highest correlated predictors. We used techniques of feature selection, including XGBoost classification, and apart from that, we used Recursive Feature Elimination (RFE) to determine the importance of features. This analysis identified ammonium and calcium as the top two predictors. These nutrients played a vital role in maintaining the paired cultivation system and promoting the robust development of Nile tilapia fish and water spinach. This process of detecting and distributing nutrients persists until the desired quantities of ammonium and calcium are reached. During each cycle, 0.7 g of ammonium sulfate and calcium nitrate are distributed, and the nutrient levels are assessed. Vernier sensors were employed for assessing nutrient values, and a system of actuators was integrated to supply the necessary nutrients to the smart farming environment using the closed-loop concept. This research investigates water quality management practices in fish farming, assesses their impact on fish health and profitability, identifies key water quality predictors, and implements a closed-loop system for nutrient delivery.

Co-Authors - STMIK AMIKOM Yogyakarta, Emha Taufiq Luthfi A, Ferry Wahyu Abdulloh, Ferian Fauzi Adelia Octora Pristi Sahida Adelia Octora Pristisahida Adha, Munirah Fatimah Aditya Wahyu Pratama Afrianto, Novan Afrig Aminuddin Ahmad Asyhari Akhmad Fakhurrozi Alif Yudha Suryatama Aristianda, Hedry Asadulloh, Bima Pramudya Aulia, Kunni Rofiqotul Azzahro, Marissa Bledug Kusuma Diyon Saputro Elsa Virantika Febrianto, Wimar Ferry Wahyu A Hedry Aristianda Heni Pujiastuti Hidayat, Prasetyo Nur Hikmah, Harisna I Nyoman Apraz Ramatryana Ihwan Mualana Ilham Unggara Irwan Novianto Jumanur Rohman Kusnawi Kusnawi Kusrini . Laksana, Feri Febria Lilis Kurniasari M Andika Fadhil Eka Putra M Rifki Ramdani Majid Rahardi Millaty, Marosimy Mualana, Ihwan Muhamad Adhitya Saputra Muhamad Nasruddin Manaf Ngulum, Mochamad Bahrul Novan Afrianto Nur Azmi Ainul Bashir NURUL FAIZAH P, Wigar Kumara Prasetyo Nur Hidayat Puji Prabowo, Dwi Ramdani, M Rifki Restiadi Bayu Taruno Rifqi Syarif Nasrulloh Rio Ardiansyah saifulloh Saifulloh, saifulloh Septian Rico Hernawan Suryatama, Alif Yudha Taruno, Restiadi Bayu unggara, Ilham Wigar Kumara P Wimar Febrianto Yana Hendriana Yogananda, Amrina Amalia Yudho Bismo Utomo, Yudho Bismo Zulfatun Ruscitasari Zulfatun Ruscitasari Zulkhairi Zulkhairi Sofyan

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