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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Pendidikan Kreano, Jurnal Matematika Kreatif-Inovatif International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Buletin Ilmiah Sarjana Teknik Elektro Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Journal of Soft Computing Exploration Devotion: Journal of Research and Community Service Unnes Journal of Mathematics Education Unnes Journal of Mathematics Journal of Advances in Information Systems and Technology Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Innovative: Journal Of Social Science Research Recursive Journal of Informatics Scientific Journal of Informatics Journal of Economic Resilience and Sustainable Development Jurnal Abdi Negeri Recursive Journal of Informatics
Endang Sugiharti, Endang
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Efektifitas Pengelolaan Manajemen Pergudangan Terhadap Sistem Distribusi Beras pada Pemerintah Daerah DKI Jakarta Heryadi, Muhammad Heri; Nofrisel, Nofrisel; Sugiharti, Endang; Simarmata, Juliater; Anggara, Dian Christopher
Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Vol. 11 No. 1 (2024): Maret
Publisher : Institut Transportasi dan Logistik Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54324/j.mtl.v1i1.1368

Abstract

DKI Jakarta experienced the rice production deficit of 99.61% in 2020. The problem arised because DKI Jakarta could fulfill its rice necessity from other cities and the poor management of the warehouse and distribution system. The aim of the study is to propose a warehouse management to improve the effective distribution system. The study used qualitative descriptive research method. There were 16 officials of the warehouse management and distribution system in PT. Tjipinang Jaya Food Station as the participants. The data was processed using the credibility, transferability, dependability and confirmability tests approaches. The results of the study indicate that the warehouse management and distribution system in PT. Tjipinang Jaya Food Station is considered ineffective and inefficient in improving food security operation in DKI Jakarta. The evidence found is the absorption or the less than demand purchase of the product, which is 2,665 tons with a total demand of 688,226 tons. The problem with inequality distribution, decreased quantity in the distribution process, raw materials received delay, unfulfilled delivery of finished material to consumers and uncertainty of the warehouse utilization planning are factors needed to be restored continuously in the future.
Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results Pradana, Dany; Sugiharti, Endang
Recursive Journal of Informatics Vol 1 No 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v1i1.63964

Abstract

Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results. Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons. Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing. Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.
Optimization of support vector machine using information gain and adaboost to improve accuracy of chronic kidney disease diagnosis Listiana, Eka; Muzayanah, Rini; Muslim, Much Aziz; Sugiharti, Endang
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.218

Abstract

Today's database is growing very rapidly, especially in the field of health. The data if not processed properly then it will be a pile of data that is not useful, so the need for data mining process to process the data. One method of data mining used to predict a decision in any case is classification, where in the classification method there is a support vector machine algorithm that can be used to diagnose chronic kidney disease. The purpose of this study is to determine the level of accuracy of the application of information gain and AdaBoost on the support vector machine algorithm in diagnosing chronic kidney disease. The use of information gain is to select the attributes that are not relevant while AdaBoost is used as an ensemble method commonly known as the method of classifier combination. In this study the data used are chronic kidney disease (CKD) dataset obtained from UCI repository of machine learning. The result of experiment using MATLAB applying information gain and AdaBoost on vector machine support algorithm with k-fold cross validation default k = 10 shows an accuracy increase of 0.50% with the exposure of the result as follows, the support vector machine algorithm has accuracy of 99.25 %, if by applying AdaBoost on the support vector machine has an accuracy of 99.50%, whereas if applying AdaBoost and information gain on the support vector machine has an accuracy of 99.75%.
Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures Auni, Ahmad Ramadhan; Sugiharti, Endang
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15169

Abstract

Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.
Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Peran Kecerdasan Buatan Generatif Bagi Peningkatan Kompetensi Guru di SMA Muhammadiyah 2 Semarang Setiawan, Abas; Arifudin, Riza; Sugiharti, Endang; Abidin, Zaenal; Al Hakim, M. Faris; Choirunnisa, Rizkiyanti; Subarkah, Agus
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2952

Abstract

Tantangan yang saat ini dibutuhkan oleh guru SMA adalah menciptakan inovasi pembelajaran berbasis teknologi. Saat ini kecerdasan buatan (AI) telah muncul sebagai solusi potensial untuk meningkatkan kualitas pembelajaran di tengah pesatnya kemajuan teknologi. Namun, geografi Indonesia yang luas membuat fasilitas teknologi pendidikan belum merata. Oleh karena itu, guru di SMA Muhammadiyah 2 Semarang perlu meningkatkan kompetensi literasi digitalnya, terutama untuk teknologi terkini. Program ini telah berhasil membuka wawasan guru terhadap teknologi baru dan memberikan keterampilan praktis dalam mengintegrasikan teknologi Kecerdasan Buatan Generatif ke dalam proses pembelajaran. Para guru diberikan pembekalan penggunaan teknologi ChatGPT dan Gemini untuk mempersiapkan bahan ajar. Hasil evaluasi menunjukkan bahwa guru mampu memahami dan mulai menerapkan teknologi AI dalam pembuatan materi ajar, serta merasa termotivasi untuk terus menggunakannya secara berkelanjutan dalam pembelajaran.
Pendampingan Pemanfaatan Aplikasi Berbasis Kecerdasan Buatan bagi Guru di Yayasan Waqah Al Hidayah, Hatyai, Provinsi Songkhla, Thailand Abidin, Zaenal; Arifudin, Riza; Sugiharti, Endang
Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 1 (2025): JIPPM - Juni 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jippm.758

Abstract

Pengabdian kepada masyarakat ini bertujuan meningkatkan literasi digital guru melalui pemanfaatan teknologi kecerdasan buatan (AI) dalam pembelajaran di Yayasan Waqaf Al-Hidayah, Hatyai, Thailand. Tantangan penggunaan teknologi di kalangan guru di Hatyai, Provinsi Songkhla menjadi perhatian utama, khususnya dalam konteks pembelajaran yang relevan bagi generasi digital. Metode kegiatan mencakup lima tahapan: identifikasi kebutuhan, pengenalan AI, praktik penggunaan agen cerdas berbasis Large Language Model (LLM), pengembangan media pembelajaran berbasis Canva AI, serta pendampingan dan monitoring melalui media sosial. Hasil kegiatan menunjukkan bahwa seluruh peserta telah menggunakan aplikasi berbasis AI sebelumnya, dan menganggap AI membantu efisiensi dan kreativitas pembelajaran. Namun, kekhawatiran terhadap ketergantungan dan dampak sosial juga muncul. Evaluasi melalui angket mengungkapkan respons positif terhadap kegiatan, meskipun partisipasi terbatas akibat kendala geografis. Simpulan dari kegiatan ini adalah bahwa pendampingan pemanfaatan AI dapat memperkuat kompetensi guru, namun tetap diperlukan penguatan etika penggunaan teknologi untuk menjaga kualitas interaksi dan keberimbangan peran guru dalam proses pembelajaran.
Analysis of Indonesian Port Integration System Performance (Inaportnet) Based On Pieces Framework Variables In 2021 Sekarwati Ariadi, Tiara; Malisan, Johny; Sugiharti, Endang
Devotion : Journal of Research and Community Service Vol. 3 No. 14 (2022): Special Issue
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/dev.v3i14.310

Abstract

This study is aimed at identifying the variables of the Indonesian Port Integration (Inaportnet) System Performance Based on the Piece Framework Variables in 2021. The built model includes explanations. Six independent variables (independent) and one dependent variable (dependent). Variables that have positions as independent variables are performance, information and data, economy, control and security, efficiency, and service. The variable that acts as a variable is user satisfaction. Data analysis was performed using the Multiple Linear Regression Analysis approach. The data used are primary, quantitative, and cross-sectional data. The data were collected from 80 respondents by distributing questionnaires using a Likert scale with five answer choices. Data analysis was carried out with the help of the SPSS application. The results of testing variables, namely performance, and economics, show that there is a significant influence on the user satisfaction variable, while for the variables, information and data, control and security, efficiency, and service do not have a significant effect on the user satisfaction variable, it is evident from the results of the T test where the performance variable, and economics showed a significant level less than 0.05, and was positive for user satisfaction, and the information and data, control and security, efficiency, and service showed variables a significant level greater than 0.05 , and declared no positive effect on user satisfaction
The Effect of Augmented Reality Acceptance on E-Commerce on Cosmetic Purchase Decisions Using Combination TPB and TAM Khoirunnisa, Oktaria Gina; Sugiharti, Endang
Journal of Advances in Information Systems and Technology Vol 5 No 2 (2023): October
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v5i2.63743

Abstract

The fact that the sales of cosmetic products in Indonesia are increasing causes competition between brands to be unavoidable. One of the strategies the company prepared is to expand its marketing reach with e-commerce. But when selling cosmetic products by online new problem arises, scilicet the absence of a tester causes a lack of information about the product and how the technology is accepted. A lack of understanding about the product will affect consumer buying interest. Shopee answers this problem by providing a markerless augmented reality-based beauty cam feature. Based on this description, this study will analyze the effect of acceptance of the use of augmented reality on product purchase decisions using a combination of the Technology Acceptance Model and Theory of Planned Behavior. Data in his study was collected by distributing online questionnaires to Shopee users who have used this feature. The results of this study indicate that behavioral control variables do not affect a person's behavioral intention to use the beauty cam feature or the intention to buy cosmetic products. In addition to these correlations, all proposed correlations have a significant effect. The results of this study can stimulate future research and become a consideration for feature developers and business owners in other fields.
XGBoost Algorithm on Intrusion Detection System in Detecting Network Intrusions Hernowo, Mutiara; Sugiharti, Endang
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.9105

Abstract

Saat ini, teknologi sudah menjadi kebutuhan manusia. Akibat peningkatan penggunaan internet, banyak paket data yang diteruskan ke lalu lintas jaringan tempat data berkomunikasi antara dua titik akhir (transmisi data). Aktivitas ini harus aman karena informasi pribadi pengguna bersifat rahasia. Jaringan memiliki sistem untuk menganalisis setiap data yang melewati lalu lintas dan mendeteksi data berbahaya, yang disebut Intrusion Detection System (IDS). IDS membutuhkan model deteksi untuk meningkatkan kinerjanya dalam mendeteksi intrusi. Tujuannya adalah untuk mengimplementasikan algoritma XGBoost untuk meningkatkan skor akurasi kinerja IDS menggunakan metode yang diusulkan. Dalam tulisan ini, kami mengusulkan model deteksi menggunakan algoritma XGBoost dan Sequential Feature Selection (SFS) sebagai metode pemilihan fitur. Metode-metode ini telah diuji pada dataset NSL-KDD. Melalui penelitian implementasi model yang diusulkan ini, diperoleh hasil dengan menganalisis metrik evaluasi seperti, akurasi, presisi, recall, dan f1-score. Hasilnya menunjukkan skor akurasi mencapai 99,24%. Dengan kata lain, hasilnya cukup tinggi dibandingkan penelitian sebelumnya. Dengan demikian, metode yang diusulkan dapat digunakan untuk meningkatkan kinerja IDS guna mendeteksi intrusi dan membantu jaringan menjadi lebih aman. Penelitian ini masih memerlukan pengembangan untuk penelitian selanjutnya karena teknologi terus berkembang.
Co-Authors Abas Setiawan Adha, Nugraha Saputra Adi, Pungky Tri Kisworo Adi, Pungky Tri Kisworo Afifah, Eka Nur Afifah, Eka Nur Aji, Akbar Lintang Al Hakim, M. Faris Alamsyah - Amin Suyitno Anggara, Dian Christopher Anggyi Trisnawan Putra Arief Broto Susilo Astuti, Winda Try Astuti, Winda Try Asyrofiyyah, Nuril Atikah Ari Pramesti, Atikah Ari Auni, Ahmad Ramadhan Boro, Fabian Dominggus Eka Budi Prasetiyo, Budi Bunardi, Gunawan Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Devi, Feroza Rosalina Devi, Feroza Rosalina Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Dwika Ananda Agustina Pertiwi Emi Pujiastuti Fauzan, Riantama Sulthana Fitriana, Erma Nurul Florentina Yuni Arini, Florentina Yuni Hakim, M. Faris Al Hani'ah, Ulfatun Hariyanto, Abdul Hernowo, Mutiara Heryadi, Muhammad Heri Imam Sonny, Imam Indah Urwatin Wusqo Isa Akhlis Juliater Simamarta Jumanto Jumanto, Jumanto Jumanto Unjung Khoirunnisa, Oktaria Gina Korzhakin, Dian Alya Krida Singgih Kuncoro Kuncoro, Rizki Danang Kartiko Kurniawati, Putri Aida Nur Lestari, Dewi Indah Listiana, Eka Malisan, Johny Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Much Aziz Muslim Muhammad Kharis Mulyono Mulyono Muzayanah, Rini Nofrisel, Nofrisel Nugroho, Prisma Bayu Perbawawati, Anna Adi Perbawawati, Anna Adi Pipit Riski Setyorini Pradana, Dany Pradhana, Fajar Eska Purnamasari, Ratnaningtyas Widyani Raharjo, Ahmad Solikhin Gayuh Ratri Rahayu Riza Arifudin Rofik Rofik, Rofik Rupiah, Siti S.Pd. M Kes I Ketut Sudiana . Sampurno, Global Ilham Sampurno, Global Ilham Sari, Firar Anitya Sekartaji, Novanka Agnes Sekarwati Ariadi, Tiara Subarkah, Agus Sugiman Sugiman Sugiman Sukestiyarno Sukestiyarno Sukmadewanti, Irahayu Sukmadewanti, Irahayu Sulis Eli Triliani, Sulis Eli Sungkowo, Nanang Supriyono Supriyono Susanti, Eka Lia Sutarti, Sri Sutarti, Sri Umi Latifah Vedayoko, Lucky Gagah Vedayoko, Lucky Gagah Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wijaya, Henry Putra Imam Zaaidatunni'mah, Untsa Zaenal Abidin