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Ningsih, Lidya
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Tradisi Jamuan Laut Persfektif Islam (Studi Nelayan Pantai Kelang, Desa Sei Naga Lawan Kabupaten Serdang Bedagai) Datmi, Akbar Rosyidi; Nasution, Mhd. Abdul Hakim; Sabana, M. Fauzi; Ningsih, Lidya
JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Vol 22 No 2 (2023): September 2023
Publisher : LP2M STAI Miftahul 'Ula (STAIM) Nganjuk

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/lentera.v22i2.1227

Abstract

This study discusses the relationship between tradition and religion carried out by the fishing community of Pantai Kelang, Sei Naga Lawan Village. The question is asked how the tradition of sea banquets has an Islamic perspective. This paper uses the field study method to describe data clearly and in detail on a phenomenon or event. Sources were obtained through observation, documentation and interviews with village heads, sea handlers, fishermen leaders and community leaders. Data were analyzed through the stages of data reduction, data presentation and drawing conclusions. The results show that the tradition of sea banquets carried out by the fishing community of Pantai Kelang, Sei Naga Lawan Village, in practice does not conflict with Islamic sharia rules, but belief in sea ghosts or sea genies is an act of shirk. Because Islam emphasizes that sustenance and safety on the sea and on land are the sole power of Allah. The relationship between tradition and religion has sharia boundaries that must be understood by the community, especially the fishermen of Kelang Beach, Sei Naga Lawan Village.
Sportifitas dalam Keolahragaan sebagai Bagian Pembentukan Sikap Generasi Muda Terkhusus dalam Pelaksanaan Kegiatan Olahraga Afrizal, Haikel; Darmanto, Ferry; W, Imam Santoso Ciptaning Wahyu; Izatinada, Rozaqiatussalwa; Putri, Nada Kamilia; Rahmah, Rosida Aulia; Galang, Faiz; Ningsih, Lidya; Nisa’, Hanya Asa Khoirun; Utami, Nadia Tri
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Olahraga dapat dianggap sebagai cerminan kehidupan. Setiap hari, manusia melakukan berbagai gerakan, yang juga tercermin dalam olahraga, terutama dalam berbagai permainan. Pendidikan jasmani bertujuan untuk membentuk sikap generasi muda, dengan fokus pada pengembangan sikap sportif. Memiliki jiwa sportif sangat penting, karena nilai-nilai tersebut berperan dalam membentuk generasi muda yang menghasilkan, baik dalam dunia olahraga maupun dalam kehidupan sehari-hari, guna mencegah penyimpangan sikap. Metode yang digunakan dalam penelitian ini adalah refleksi literatur, yang mencakup pencarian jurnal dan sumber-sumber yang relevan di Google Scholar. Hasil pengamatan menunjukkan bahwa olahraga dan pendidikan jasmani memang berperan penting dalam membentuk sikap sportif pada generasi muda, yang diajarkan melalui pembelajaran pendidikan jasmani di sekolah. Generasi muda mengajarkan untuk praktik sportif, yang mencakup nilai-nilai seperti kejujuran, tanggung jawab, disiplin, dan kerja sama. Selain itu, mereka juga mengajar untuk menerima kekalahan, menghormati lawan, serta menghargai diri sendiri dan orang lain. Olahraga yang dilakukan secara rutin dan teratur dapat membentuk sikap dan jiwa sportif yang bermanfaat, baik dalam dunia atlet atau kehidupan sehari-hari.
Pengenalan Berpikir Komputasional Melalui Tantangan Bebras 2024: Studi Implementasi di Bandung Raya Rachmawati, Ema; Ciptasari, Rimba Whidiana; Ningsih, Lidya; Firdaus, Fauzan
Jurnal Pengabdian Masyarakat Akademisi Vol. 4 No. 2 (2025)
Publisher : Jurnal Pengabdian Masyarakat Akademisi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/jpma.v4i2.1265

Abstract

Peningkatan kemampuan berpikir komputasional di kalangan pelajar menjadi penting di era digital, terutama untuk mempersiapkan mereka menghadapi tantangan teknologi. Pengabdian masyarakat ini dilakukan melalui workshop Tantangan Bebras 2024 yang bertujuan untuk mengenalkan konsep Berpikir Komputasional (BK) kepada siswa di jenjang SD, SMP, dan SMA. Kegiatan ini mencakup pelatihan soal-soal interaktif, penerapan strategi pemecahan masalah, dan simulasi Tantangan Bebras secara daring. Dengan melibatkan 814 peserta, program ini dirancang untuk meningkatkan minat siswa dalam STEM dan mempersiapkan mereka menghadapi Tantangan Bebras 2024. Metode pelaksanaan meliputi koordinasi dengan mitra, pengelolaan data peserta, dan simulasi melalui Learning Management System (LMS). Hasilnya menunjukkan partisipasi aktif siswa dan peningkatan pemahaman mereka terhadap BK. Program ini diharapkan dapat memberikan kontribusi signifikan terhadap peningkatan kualitas pendidikan berbasis teknologi di Indonesia. Kata kunci: Berpikir Komputasional; Tantangan Bebras; STEM; Pendidikan
Unplugged Coding Activity sebagai Pendekatan Pengenalan Konsep Berpikir Komputasional Siswa Madrasah Aliyah Ciptasari, Rimba Whidiana; Rachmawati, Ema; Ningsih, Lidya
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.v8i2.2953

Abstract

Salah satu tantangan dalam pembelajaran berpikir komputasional siswa pada sekolah madrasah adalah muatan informatika masih berperan sebagai mata pelajaran pilihan atau ekstrakurikuler. Disamping itu, terbatasnya ketersediaan perangkat komputer sebagai media pembelajaran juga menjadi tantangan lainnya. Berdasarkan kondisi tersebut, pada 2024, Biro Bebras Universitas Telkom mengadakan pelatihan pengenalan berpikir komputasional berbasis unplugged coding activity kepada siswa madrasah aliyah Ponpes Modern Assuruur Kabupaten Bandung. Terdapat tiga jenis permainan unplugged yang diberikan, yaitu RoboKnapsack, Railway Line, dan Sorting Network. Evaluasi tingkat pemahaman siswa dilakukan melalui pretest dan posttest dengan tingkat kesulitan soal posttest yang lebih tinggi. Hasilnya menunjukkan sekitar 28,57% para siswa masih mampu mempertahankan nilai bahkan memperoleh nilai yang lebih tinggi. Hal ini menunjukkan efektivitas unplugged coding activity sebagai strategi awalan untuk mengenalkan konsep berpikir komputasional.
Comparative Analysis of Weighted-KNN, Random Forest, and Support Vector Machine Models for Beef and Pork Image Classification Using Machine Learning Satria, Budy; Afrianto, Nurdi; Ningsih, Lidya; Sakinah, Putri; Sidauruk, Acihmah; Mayola, Liga
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3736

Abstract

The actual problem that occurs in the sale of meat by some conventional market traders is mixing beef with pork because of the high selling price. The difference between pork and beef lies in the color and texture of the meat. However, many people do not understand this difference. This study aims to provide a solution to distinguish the two types of beef through a classification process by obtaining the best accuracy using the W-KNN, RF, and SVM models based on machine learning. This study compares the model's performance based on the number of datasets, comprising 400 original images (200 beef and 200 pork images), using a 80:20 ratio for training and test data. The extraction process uses two algorithms: HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue). The model evaluation uses a confusion matrix that includes accuracy, Precision, Recall, and F1-score. Based on the results of the model testing, it was found that the random forest algorithm gave the best overall results, with the highest accuracy of 98.75%, Precision of 97%, F1-score of 98%, and recall of 99% on the number of decision trees of 400. This shows the stability and generalization of the superior model. The random forest algorithm is the most effective for classifying beef and pork data with minimal errors. Implications for further research include using a deep learning approach, especially for image processing, to detect differences in each meat characteristic and increase accuracy.
Harnessing Machine Learning for Stock Price Prediction with Random Forest and Simple Moving Average Techniques Priyatno, Arif Mudi; Ningsih, Lidya; Noor, Muhammad
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.
Predict Students' Dropout and Academic Success with XGBoost Ridwan, Achmad; Priyatno, Arif Mudi; Ningsih, Lidya
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.
Perbandingan Kinerja Algoritma Klasifikasi Status Mutu Air Ningsih, Lidya; Jaman, Jajam Haerul; Salam, Naufal Ibnu; Haikal, Muhammad
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.
Co-Authors Achmad Ridwan, Achmad Acihmah Sidauruk Afrianto, Nurdi Afrizal, Haikel Arif Mudi Priyatno Budy Satria Darmanto, Ferry Datmi, Akbar Rosyidi Ema Rachmawati Firdaus, Fauzan Galang, Faiz Izatinada, Rozaqiatussalwa Jaman, Jajam Haerul Liga Mayola Muhammad Haikal Muhammad Noor Nasution, Mhd. Abdul Hakim Nisa’, Hanya Asa Khoirun Putri, Nada Kamilia Rahmah, Rosida Aulia Rimba Whidiana Ciptasari Sabana, M. Fauzi SAKINAH, PUTRI Salam, Naufal Ibnu Utami, Nadia Tri W, Imam Santoso Ciptaning Wahyu