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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Dialektika Informatika (Detika) Indonesian Community Journal Journal of Artificial Intelligence and Digital Business Indonesian Journal of Multidisciplinary on Social and Technology Journal of Engineering and Science Application As-Salam: Journal Islamic Social Sciences And Humanities Journal of Education and Computer Applications
Lidya Ningsih
Telkom University, Jawa Barat, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Fuzzy Learning Vector Quantization Untuk Klasifikasi Citra Daging Oplosan Berdasarkan Ciri Warna dan Tekstur Lidya Ningsih; Agus Buono; Mushthofa; Toto Haryanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.932 KB) | DOI: 10.29207/resti.v6i3.4067

Abstract

Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed.
PERBANDINGAN PROVINSI JAWA BARAT DAN PROVINSI DKI JAKARTA DALAM PEMANFAATAN SISTEM INFORMASI PENANGANAN COVID-19 Adini Yusmar; Agung Pambudi; Hery Wibowo; Lidya Ningsih; Syifa Shabrina Siregar; Auzi Asfarian
Jurnal Dialektika Informatika (Detika) Vol 1, No 2 (2021): Jurnal Dialektika Informatika (Detika) Vol.1 No.2 Mei 2021
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/detika.v1i2.5853

Abstract

Indonesia memiliki kasus positif COVID-19 cukup tinggi di Asia Tenggara dengan jumlah mencapai 287.000 kasus sampai dengan September 2020. Upaya pemerintah Indonesia dalam menangani pandemi salah satunya melalui pemanfaatan sistem informasi berupa website baik skala nasional maupun provinsi. Penelitian ini bertujuan untuk mengevaluasi pelayanan sistem informasi website dalam menangani pandemi COVID-19 di Provinsi Jawa Barat dan DKI Jakarta. Metode yang digunakan adalah E-Governance Online-Service Quality (EGOSQ) mencakup kriteria Quality, Usability, Emancipation, dan Performance dengan hasil pengolahan data menggunakan Skala Likert. Responden berdomisili di Provinsi Jawa Barat dan DKI Jakarta dengan profil berdasarkan jenis kelamin, usia, profesi dan juga kemampuan literasi komputer. Hasil penelitian menunjukkan Indeks Persentase Provinsi Jawa Barat sebesar 77,42% dan DKI Jakarta sebesar 73,72% dimana kedua provinsi tersebut termasuk dalam kategori BAIK dalam memanfaatkan sistem informasi website untuk penanganan COVID-19.
TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets Arif Mudi Priyatno; Lidya Ningsih
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): 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.v11i3.1995

Abstract

Twitter is a social media service that is often used (popular) as a means of communication between users. Twitter's popularity makes spammers spam for personal purposes and gains. Bot spammers are user abuse on Twitter social media. Spammers spread spam repeatedly to other users. This spam is done with the aim of achieving trending topics. Spam activity is carried out by imitating the behavior patterns of real users so that they are not detected as acts of Twitter abuse. in this paper proposed a TF-IDF weighting to detect spammer accounts on Twitter based on tweets and retweet representation of tweets. The purpose of this study is to detect Bot Spammers or Humans using a classification technique using the Naive Bayes algorithm. The best experimental results in the division of 70% training data and 30% test data obtained 92% accuracy with precision and recall of 100% and 87.5%, respectively. This shows that it has successfully detected spammer accounts on Twitter.
Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm Lidya Ningsih; Putri Cholidhazia
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 1 No. 1 (2022)
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.53 KB) | DOI: 10.31004/riggs.v1i1.10

Abstract

Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. According to (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One way that can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processing techniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue and Hue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simple with good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highest accuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classified the maturity of tomatoes by utilizing the color image of RGB and HSV.
Perbandingan Kinerja Algoritma Klasifikasi Status Mutu Air Lidya Ningsih; Jajam Haerul Jaman; Naufal Ibnu Salam; Muhammad Haikal
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 2 No. 1 (2024)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijmst.v2i1.298

Abstract

Klasifikasi mutu air adalah salah satu teknik dalam melakukan penilaian terhadap air sebagai objek penelitian. Penelitian ini dilakukan dengan tujuan agar dapat memberikan pengetahuan terhadap mutu atau kualitas air, sehingga dapat menjadi solusi terbaik yang dapat dilakukan terhadap air tersebut sebelum dikonsumsi. Penelitian ini menggunakan dua skenario dengan beberapa teknik klasifikasi, diantaranya adalah Algoritme Naive Bayes, KNN, Multiclass classification, MLP, SVM, dan Random Forest. Berdasarkan hasil peneltiian yang dilakukan dengan beberapa algoritma klasifikasi tersebut, didapatkan hasil akurasi terbaik menggunakan Algoritme Random Forest dengan persentase akurasi sebesar 99,5% pada skenario pertama dan 99,7% pada skenario kedua . Sedangkan tingkat akurasi terendah ditemukan pada Algoritme Naive Bayes dengan persentase akurasi sebesar 22,3% pada skenario pertama dan 21,9% pada skenario kedua. Hal ini disebabkan karena dataset mutu air yang diperoleh tidak seimbang atau tidak terdistribusi normal (Gaussian). Selain itu, algoritme Naive Bayes memiliki kinerja baik dalam pekerjaan klasifikasi dengan data teks.
Harnessing Machine Learning for Stock Price Prediction with Random Forest and Simple Moving Average Techniques Arif Mudi Priyatno; Lidya Ningsih; Muhammad Noor
Journal of Engineering and Science Application Vol. 1 No. 1 (2024): April
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i1.1

Abstract

This paper explores the application of machine learning in predicting stock price trends, specifically for PT Bank Central Asia Tbk (BBCA) shares, using the Random Forest Regression model and Simple Moving Average (SMA) techniques. The SMA parameters ranged from 3 to 200 days, aiding in forecasting the price trends as either rising, sideway, or declining. To achieve accurate and generalizable predictions, the data normalization process was implemented using the MinMax scaler. The methodological framework adopted a time series cross-validation (CV) approach, executed 10 times with a future test window of 40 days, ensuring the robustness and reliability of the predictive model. The model's performance was systematically evaluated based on metrics of accuracy, recall, precision, and F1-score. Results from the cross-validation series indicated varied performance, with the most notable achievements in the 9th and 10th iterations, where both demonstrated an F1-score surpassing 0.745 and 0.808 respectively, and similar levels of accuracy and recall at 0.825. These high F1-scores signify a strong harmonic balance between precision and recall, underscoring the model's capability to effectively predict the stock price movements of BBCA. The findings affirm the potential of utilizing advanced machine learning techniques like Random Forest in conjunction with SMA indicators to enhance the predictability of stock market trends, offering valuable insights for investors and financial analysts.
Pendampingan Implementasi Materi Berpikir Komputasional di Mata Pelajaran Informatika Pada Kurikulum Merdeka Tingkat SMP di Wilayah Kabupaten Bandung Ema Rachmawati; Fazmah Arif Yulianto; Lidya Ningsih
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.4899

Abstract

In the era of globalization and digitalization, mastery of Computational Thinking (CT) has become crucial in computer science education, especially at the junior high school level. This mentoring program is designed to bridge the gap between the evolving curriculum demands and teachers' readiness to implement them, with a specific focus on CT content in the Informatics subject for junior high schools. The activities involve teaching technical aspects of informatics and providing a deep understanding of computational thinking as an essential tool for developing students' analytical, creative, and problem-solving abilities. By enhancing the capabilities of junior high school informatics teachers, this program aims to improve the quality of education and equip teachers with relevant skills to teach CT material to students, thereby contributing to community empowerment through the overall enhancement of digital literacy.
Predict Students' Dropout and Academic Success with XGBoost Achmad Ridwan; Arif Mudi Priyatno; Lidya Ningsih
Journal of Education and Computer Applications Vol. 1 No. 2 (2024)
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jeca.v1i2.13

Abstract

The attrition rate of students in higher education is a worldwide issue that profoundly affects both individuals and institutions. Students who fail to complete their studies often encounter economic and social difficulties, while educational institutions suffer a deterioration in reputation and operational efficacy. This paper proposes the creation of a prediction model utilizing the XGBoost algorithm to assess students' academic progress and dropout risk. The model incorporates several elements, such as academic, demographic, and socio-economic, to yield comprehensive insights into students' educational trends. This research utilizes the Predict Students' Dropout and Academic Success dataset, comprising 4,424 data points and 36 attributes. The data underwent normalization via StandardScaler and was divided into five scenarios for training and testing, ranging from a 50:50 to a 90:10 split. The evaluation of the model was conducted utilizing accuracy, precision, recall, and F1-Score criteria. The findings indicate that the model attains peak performance in the 80:20 scenario, exhibiting 88% precision and an 81% F1-Score, signifying an ideal equilibrium between predictive accuracy and risk identification capability. This study demonstrates that XGBoost can serve as a dependable predictive instrument to aid decision-making in the education sector. These findings establish a foundation for formulating targeted interventions aimed at enhancing student retention. Subsequent study may investigate the use of real-time data and sophisticated models to enhance predictive accuracy.
Exploring Educational Insights: An In-depth Analysis of Al-Quran Interpretation of the Fasting Verse for Contemporary Islamic Pedagogy Lidya Ningsih; Laud Pulungan; Bariqy; Harun Al-Rasyid
Getpress Management Journals Vol 1 No 2 (2023): AS-SALAM: Journal Islamic Social Sciences and Humanities
Publisher : Yayasan Salam Cerdas Al-Fattah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This article aims to explore educational insights with an in-depth analysis of the interpretation of the Koran regarding fasting verses, the fasting verses used, namely QS al-Baqarah verses 183 to 187. In this article the thematic method is used, namely the method used to search for themes. -related themes in the Koran with the correct steps in it, so that in searching the verses it is connected to a complete understanding of the verses that talk about fasting. The findings explain that contemporary Islamic education utilizes these verses as a basis for forming individuals who not only know religious teachings, but are also able to apply moral, ethical and spiritual values in everyday life. Verses 183-187 of Surah Al-Baqarah provide a strong foundation in forming character and an attitude of piety, creating individuals who are tough, responsible and have a deep understanding of Islamic teachings. In conclusion, contemporary Islamic education must be able to combine the fundamental values contained in these verses, involving spiritual, moral, ethical and inclusive aspects in the learning approach.
Co-Authors Achmad Ridwan Adini Yusmar Agung Pambudi Agus Buono Arif Mudi Priyatno Auzi Asfarian Bariqy Cholidhazia, Putri Ema Rachmawati Fazmah Arif Yulianto Harun Al-Rasyid Hery Wibowo Jaman, Jajam Haerul Laud Pulungan Muhammad Haikal Muhammad Noor Mushthofa Naufal Ibnu Salam RR. Ella Evrita Hestiandari Syifa Shabrina Siregar Toto Haryanto