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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Knowledge Engineering and Data Science IT JOURNAL RESEARCH AND DEVELOPMENT JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EXPLORE TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal SITECH : Sistem Informasi dan Teknologi Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Journal of Innovation Information Technology and Application (JINITA) Journal of Information Technology and Its Utilization Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Vivabio : Jurnal Pengabdian Multidisiplin Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini) JUSTIN (Jurnal Sistem dan Teknologi Informasi) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer JAIA - Journal of Artificial Intelligence and Applications EXPLORE Jurnal Bina Komputer JAPI: Jurnal Akses Pengabdian Indonesia Data Sciences Indonesia (DSI) Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi
Puspitasari, Novianti
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin Haviluddin; Herman Santoso Pakpahan; Novianti Puspitasari; Gubtha Mahendra Putra; Rima Yustika Hasnida; Rayner Alfred
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p122-128

Abstract

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.
Chili Classification Using Shape and Color Features Based on Image Processing Sihombing, Yobel Fernanda; Septiarini, Anindita; Kridalaksana, Awang Harsa; Puspitasari, Novianti
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33658

Abstract

Abstract. Purpose: Chili is an agricultural product that has several varieties and is in great demand. It can be consumed directly or processed first.  This study aims to classify the types of chili using color and shape features. The types of chili are divided into five classes: cayenne pepper, green chili, big green chili, big red chili, and curly chili. The chili classification method was evaluated using three parameters: precision, recall, and accuracy.Methods: This study applied the K-Nearest Neighbors (KNN) method with the Euclidean and Manhattan distance calculation algorithm and used two feature types: color and shape. The color features were extracted based on RGB color space by obtaining the mean and standard deviation values. Meanwhile, the shape features used aspect ratio, area, and boundary.Result: The evaluation results of the classification method were able to achieve the precision, recall, and accuracy values of 1.0, which means that all test data were classified correctly. The evaluation was applied with 210 training images and 90 test images based on the test results.Novelty: This study extracted two types of features: color and shape. Those features fed the KNN method by applying the Euclidean and Manhattan distance calculation algorithm; hence, the optimal results were achieved.
Analisis Perbandingan metode ARAS dan WP Dalam Penentuan Prioritas Masyarakat Miskin Berdasarkan Sosial Ekonomi Masna Wati; Fairil Anwar; Novianti Puspitasari; Anindita Septiarini; Andi Tejawati
SISFOTENIKA Vol 13, No 2 (2023): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v13i2.1385

Abstract

Setiap daerah termasuk Kalimantan Timur dihadapkan pada permasalahan kemiskinan yang harus ditanggapi dengan serius. Pemerintah tak henti berupaya mengatasi masalah kemiskinan ini agar tercipta kondisi masyarakat yang sejahtera. Program bantuan untuk pengentasan kemiskinan merupakan upaya pemerintah dalam menyelesaikan masalah ini. Sistem Pendukung Keputusan dapat membantu pemerintah dalam membuat sebuah keputusan. Pembuatan sistem tersebut dapat menggunakan metode WP atau ARAS dengan bobot kriteria entropy sehingga perlu dianalisis metode yang paling tepat diterapkan. Kriteria keputusan berdasarkan Badan Pusat Statistik Provinsi Kalimantan Timur sebanyak 15 kriteria yaitu pengeluaran per kapita/bulan, status pekerjaan utama, jaminan kesehatan, pernah rawat inap dalam 1 tahun terakhir, pernah tidak menyantap makanan yang sehat dan bergizi, status kepemilikan tempat tinggal, luas lantai rumah, bahan utama atap rumah, bahan utama dinding rumah, bahan utama lantai rumah, sumber air utama MCK, ketersediaan fasilitas MCK, ketersediaan listrik, bahan bakar utama memasak, kepemilikan harta mobil. Hasil uji sensitivitas kedua metode diperoleh tingkat sensitivitas metode WP sebesar 0,005379 dan metode ARAS sebesar -0,118622. Hasil ini menunjukkan bahwa metode WP lebih relevan untuk digunakan dalam mengevaluasi tingkat kesejahteraan masyarakat di Kota Samarinda daripada metode ARAS.
Penerapan Metode Fuzzy Sugeno dalam Memprediksi Permintaan Darah Novianti Puspitasari; Anindita Septiarini; Olivia Octavia; Masna Wati; Heliza Rahmania Hatta
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 4 (2022)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v10i4.52152

Abstract

Transfusi darah dibutuhkan ketika seorang manusia kehilangan banyak darah. Darah tersebut disediakan oleh pusat penyimpanan darah yang bertugas memperkirakan ketersediaan stok darah agar jumlah darah selalu tercukupi. Informasi terkait stok persediaan darah sangat diperlukan karena apabila stok persediaan darah tidak mencukupi maka akan berdampak pada meningkatnya kematian, sementara stok darah yang berlebihan harus dihindari karena darah memiliki masa kadaluarsa (masa simpan darah) selama 35 hari sejak darah tersebut didonorkan. Oleh karena itu, demi meminimalisir kerugian yang terjadi, maka perlu dilakukan sebuah penelitian tentang memprediksi jumlah permintaan darah yang seharusnya diterima oleh PMI dimasa yang akan datang. Penelitian ini menggunakan metode fuzzy Sugeno untuk memperkirakan jumlah permintaan darah dimasa yang akan datang. Metode ini memiliki toleransi terhadap data-data yang tidak tepat yaitu data yang belum ditentukan nilainya sehingga dapat digunakan untuk melakukan sebuah peramalan. Penelitian menggunakan data dari empat jenis golongan darah yaitu A, B, O dan AB dari bulan Januari 2017 hingga bulan Oktober 2021. Hasil pengujian validitas yang telah dilakukan menggunakan Mean Absolute Percentage Error (MAPE) dan Root Mean Square Error (RMSE) didapatkan nilai sebesar 27.55% dan 27.61, sehingga metode ini dapat dikatakan layak dan akurat dalam memprediksi jumlah permintaan darah.
Betel leaf classification using color-texture features and machine learning approach Novianti Puspitasari; Anindita Septiarini; Ummul Hairah; Andi Tejawati; Heni Sulastri
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5101

Abstract

The existence of machine learning has been exploited to solve difficulties in various fields, including the classification of leaf species in agriculture. Betel leaf is one of the plants that provide health advantages. The objective of using a machine learning approach is to classify the betel leaf species. This study involved several processes: image acquisition, region of interest (ROI) detection, pre-processing, feature extraction, and classification. The feature extraction used the combination features of color and texture. Furthermore, the classification applied four classifiers, including artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM). The evaluation in this study implemented cross-validation with a K-fold value of 5. The method performance produced the highest accuracy value of 100% using the color and texture features with the SVM classifier.
PELATIHAN APLIKASI E-COMMERCE KEPADA PELAKU UMKM SEBAGAI UPAYA MENINGKATKAN PERTUMBUHAN EKONOMI KOTA SAMARINDA DI KECAMATAN SUNGAI KUNJANG Novianti Puspitasari; Anindita Septiarini; M. Rizky Nilzamyahya; Fayza Virdana Addiza; Fathia Nuq Qamarina; Indah Wulan Lestari; Patricia Chandra; Sugandi Sugandi
Jurnal Pengabdian Siliwangi Vol 8, No 1 (2022)
Publisher : LPPM Univeristas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jsppm.v8i1.5420

Abstract

Usaha Mikro Kecil dan Menengah (UMKM) merupakan salah satu unit usaha yang berperan penting bagi sektor perekonomian Indonesia. Selama masa pandemi UMKM terbukti memiliki daya tahan yang tangguh dalam mengatasi permasalahan krisis ekonomi. Namun, pelaku UMKM dalam menjalankan usahanya memiliki kendala terkait pemasaran yang mempengaruhi kinerja usaha. Kurangnya pengetahuan teknologi terkait inovasi dan pemasaran produk juga dialami oleh pelaku UMKM di Kecamatan Sungai Kunjang. Oleh karena itu, program pengabdian masyarakat ini menggunakan strategi digital marketing berupa metode pendampingan usaha berkelanjutan berbasis teknologi yang merupakan program kemitraan masyarakat antara perguruan tinggi dengan pelaku UMKM. Kegiatan pengabdian masyarakat ini memberikan keterampilan tentang tata cara foto produk UMKM yang dapat dilakukan oleh pelaku UMKM. Selain itu, kegiatan ini memberikan edukasi berupa pengenalan e-commerce sebagai media pemasaran produk UMKM dan juga pelatihan foto produk pelaku UMKM di kawasan Kelurahan Samarinda Ilir. Program ini tidak hanya menawarkan pemecahan masalah (problem solving) mengenai pelaku usaha yang kurang memahami cara meningkatkan kinerja usahanya, tetapi juga mentransfer pengetahuan tentang digital marketing dan teknologi berbasis mobile. Kegiatan ini dilakukan dalam bentuk sosialisasi dan pelatihan tentang penggunaan aplikasi e-commerce untuk mengelola usaha, memasarkan dan mempromosikan produk. Kegiatan pengabdian ini mampu meningkatkan perekonomian dan keterampilan baik soft-skill maupun hard-skill serta ketahanan dan keberlanjutan usaha bagi pelaku usaha.
Expert System for Pest Diagnosis on Local Black Rice Plant in East Kalimantan Using the Naive Bayes Method Puspitasari, Novianti; Septirini, Anindita; Paripurna, Rian Bintang; Samsumar, Lalu Delsi
Journal of Information Technology and Its Utilization Vol 6 No 2 (2023): December 2023
Publisher : Sekolah Tinggi Multi Media "MMTC" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.6.2.5271

Abstract

Rice plant is a food crop that produces rice as the staple food for the majority of Indonesian people. Local rice which significantly contributes to fulfill the national rice consumption is black rice produced in East Kalimantan. However, local black rice often experiences crop failure due to pest attacks and environmental factors. The amount of local black rice production also continues to decrease due to limited human resources who have the skills and knowledge to diagnose pests in black rice plants. Therefore, one effort that can be made to overcome this problem is to create an expert system that can diagnose pests and diseases in black rice plants. The expert system in this research uses the Naive Bayes method, which identifies 11 types of pests that attack black rice plants and 34 symptoms caused by these pest attacks. Naive Bayes can provide information about the percentage of pests that rice plants might experience. Based on the results of the test cases, an accuracy value of80% was obtained, so the expert system built in this research can diagnose pests on black rice plants quite well.
Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia Puspitasari, Novianti; Lempas, Gidion; Hamdani, Hamdani; Haviuddin, Haviluddin; Septiarini, Anindita
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2994

Abstract

Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.
Aplikasi WM-Banking untuk Digitalisasi Pengelolaan Layanan Bank Sampah Ramli Graha Indah Samarinda Septiarini, Anindita; Puspitasari, Novianti; Adnan, Fahrizal; Yasmin, Annisa
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 7, No 2 (2023): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v7i2.12590

Abstract

Bank sampah adalah salah satu fasilitas yang mewujudkan penyelesaian masalah sampah domestik, industri kecil, dan menengah dengan prinsip 4R (reduce, reuse, recycle, dan replant). Selain itu bank sampah juga sebagai sarana dan berperan penting dalam edukasi, perubahan perilaku dalam pengelolaan sampah dan pelaksanaan ekonomi sirkular. Bank sampah menjadi satu-satunya metode yang dimiliki Pemerintah Indonesia dalam mengatasi permasalahan sampah dari sumbernya dengan melibatkan secara langsung partisipasi aktif masyarakat. Namun, para pengelola bank sampah memiliki kendala terkait manajemen pengelolaan bank sampah berupa pencatatan, keuangan, sumber daya manusia, dan pelayanan kepada nasabah yang mempengaruhi kinerja pengelola bank sampah. Kurangnya pengetahuan teknologi terkait inovasi dan manajemen juga dialami oleh mitra yaitu pengelola Bank sampah Ramli Graha Indah Kota Samarinda. Oleh karena itu, kegiatan program kemitraan masyarakat ini mengusulkan strategi digitalisasi manajemen pengelolaan dan pelayanan bank sampah berupa teknologi berbasis website dalam bentuk sistem Waste M-Banking (WM-Banking). Pengguna dari sistem tersebut adalah pihak pengelola bank sampah dan nasabahnya. Program ini diharapkan mampu menawarkan solusi dari permasalahan yang dihadapi oleh mitra terkait kurang efektifnya manajemen pengelolaan bank sampah dan pelayanannya, dan mentransfer pengetahuan tentang digitalisasi manajemen melalui kegiatan sosialisasi dan pelatihan tentang penggunaan sistem WM-Banking. Digitalisasi tersebut diterapkan berbasis website untuk memudahkan manajemen pengelolaan dan pelayanan pada bank sampah.
Klasifikasi Penderita ISPA Menggunakan Metode Naive Bayes Classifier Syarah, May Siti; Wati, Masna; Puspitasari, Novianti
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 4, No 1 (2022): Maret 2022
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v4i1.4427

Abstract

Information related to the classification of ARI disease suffered by the community in a public health is essential. This is because the public health is one of the community health centers that is a reference for treatment for the community. Public health must identify the right type of ARI disease so that treatment for ARI sufferers can be given optimally. This study classified the data of patients with ARI in a public health based on the determining factors, namely the disease suffered, age, and period of stay. Classification is carried out using the Naive Bayes Classifier method with the Confusion Matrix testing method. The results of applying the Naive Bayes Classifier method to patient data resulted in three types of ARI, namely mild, moderate and severe. The highest number of ARI patients is severe ARI. The results of the Confusion Matrix test that have been carried out prove that this method has an accuracy of 93.33% so it is suitable for use to classify ARI diseases.
Co-Authors Abu Bakar Adelowys Sinaga Adhistya Erna Permanasari Adnan, Fahrizal Afifah, Dinda Nur AHMAD ANSYORI Ahmad Suryadi, Ahmad Ahmad Wahbi Fadillah Ajay, Muhammad Aji Ayu Muvita Putri Alameka, Faza Alameka, Faza Alfajriani Alfajriani Alfredo Sinaga Ali Sholihin Alif Rifa’i Almasari Aksenta Alvito Gabbriel Saputra Ambari, Nasser Andre Ardin Maulana Anindita Septiarini, Anindita Anton Prafanto Arinda Mulawardani Kustiawan Asdar Zulkiawan Awang Harsa Kridalaksana Brins Leonard Pailan Budiman, Edy Diana, Rita Didit Suprihanto, Didit Eka Priyatna, Surya Ery Burhandenny, Aji Fahrul Agus Fairil Anwar Fajar Fatimah Farisha Rizky Amalia Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Faza Alameka Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Muhammad Firdaus, Muhammad Bambang Fornia, Daviana Dwitasari Enka Frans Karta Sayoga Sitohang Fuad, Natalie Gerda, Misselina Madya Gubtha Mahendra Putra Gunawan, Ayu Lestari Gunawan, Santika Haeruddin, Haeruddin Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hanung Adi Nugroho Hatta, Heliza Rahmania Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Hemelia, Junita Henderi . Heni Sulastri Hidayat, Ahmad Nur Hijratul Aini Iin Nurkarima Indah Wulan Lestari Irfan, Aliya Islamiyah Islamiyah Joan Angelina Widians, Joan Angelina Julius Rinaldi Simanungkalit Kalista, Nazwa Nur Maulida Qintani Kamara, Rahmat Kamila, Vina Zahrotun Kurniati, Wendy Kurniawan, Tri Basuki Lalu Delsi Samsumar, M.Eng. Laraswati, Sherina Latifa Gorriana Gusmaningrum Lempas, Gidion M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Mega Yoalifa Mewengkang, Alfrina Muhammad Abdillah Muhammad Dzacky Muhammad Firdaus Mulia, Amalia Budiana Nataniel Dengen Noval Bayu Setiawan Nur Hasanah Nurhidayat, Rifki Nurkarima, Iin Nursari, Ayla Nurul Kusuma Dewi, Nurul Kusuma Olivia Octavia Pakpahan, Herman Santoso Paripurna, Rian Bintang Pasorong, Hillary Bella Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratama, Fhanji Wilis Pusparini, Faradilla Rahayu, Ervina Rahayu, Rizqi Widya Raihanfitri Adi Kalipaksi Ramadhaniaty, Dinda Rayner Alfred Reza Nur Muhammad Rezky, Muhammad Rima Yustika Hasnida Rizky Pratama Putra Rondongalo Rismawati Rosita, Aliffia Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari, Rosmasari Rosmasari, Rosmasari Saipul, Saipul Sandyanegara, Aryasena Bela Sarira, Brayen Tisra Septiani, Reni D. Septirini, Anindita Setyadi, Hario Jati Sihombing, Yobel Fernanda Simanungkalit, Julius Rinaldi Stefanie Stefanie Sugandi Sugandi Sulastri, Heni Sumaini Sumaini Suryani Junita Patandianan Syachmiral, Zidane Althaariq Syarah, May Siti Taruk, Medi Tejawati, Andi Tjikoa, Ade Fiqri Vicky Pranandika Wijaksana Wahyudi, Moh Ikhwan Waksito, Alan Zulfikar Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wijaya, Zhienka Putri Willyardo Tampubolon Wintin, Chintia Liu Yasmin, Annisa Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zali, Wahyu Noor