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"Penelitian ini bertujuan untuk memberikan persepsi baru dalam pengukuran ketahanan pangan dengan menggunakan volatilitas harga pangan. Pendekatan data iklim digunakan untuk menganalisis pola volatilitas harga bersama dengan variabel sosial dan ekonomi. Proyeksi perubahan iklim dilakukan untuk melihat wilayah yang rentan dalam produksi pangan selanjutnya persepsi petani diukur untuk melihat kesiapan mereka untuk menghadapi perubahan iklim. Ditemukan pola adaptasi petani terhadap dampak perubahan iklim pada ketidakpastian harga pangan khususnya tingkat rumah tangga. Selanjutnya data dinamika iklim digunakan sebagai nilai input untuk pembuatan model prediksi volatilitas harga pangan. Dengan menggunakan metode statistik dan teknik jaringan syaraf tiruan, estimasi volatilias harga berhasil dihasilkan berserta dengan nilai prediksinya, luaran yang dihasilkan digunakan untuk mengoptimalkan sistem pangan yang lebih berkelanjutan.

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This study aims to provide a new perception of food security by using food price volatility to measure sustainability. The climate data approach is used to analyze patterns of price volatility along with social and economic variables. Climate change projections are carried out to look at areas that are vulnerable to food production, then farmers' perceptions are measured to see their readiness to face climate change. The pattern of farmers' adaptation to climate change on food price uncertainty, especially at the household level, was found. Furthermore, climate dynamics data are used as input values for prediction models of food price volatility. The Model using statistical methods and neural network techniques, the estimation of price volatility is successfully generated along with the predictive value, and the resulting output is used to optimize a more sustainable food system."

"The rising food price has been signaling a crisis to food insecurity among the poor since the period of 2007/2008. The poor would be in a difficult situation to allocate the budget to meet the demand for food and nonfood in daily life as the real income changes. Food insecurity measured by Food Insecurity Experience Scale (FIES) is a most recent broadened concept of food insecurity considering the existence of anxiety to food access. This research aims to evaluate the causal inference of food price exposure to the FIES both on simple sum namely raw score and Rasch scale, a corrected measure which assuming the same latent traits among the households. The estimation used is Pooled Ordinary Least Square through the multilevel observations and Panel Regression for regional-level data. The main finding of this research is that the rising food price significantly affected the FIES, consistently on the raw score and Rasch scale, specifically to the vulnerable households defined by the bottom 40 percent in terms of their expenditure. The rising food price also increased the proportion of severely food insecure households at the regional level. As the heterogeneous effect through islands is also evaluated, it's concluded that the highest effect of the rising food price to experiencing the anxiety of food insecurity belongs households located in Bali and the lowest effect belongs to households located in Java Island. Decomposing food price into rice and nonrice is solving the puzzle where and who belongs the worse effect should be. The rising rice price is affecting worse to the households in Sumatera and Papua, but on the contrary, the households in Java, Nusa Tenggara, and Sulawesi were taking benefit amid the rising rice price. The result is also serving as a baseline in evaluating the impact of such an outbreak namely Covid-19 through the channel of compensating variations regarding food insecurity. "

"Pemantauan harga komoditas strategis merupakan pekerjaan yang penting karena kontribusi signifikan yang dimiliki oleh komoditas strategis terhadap perhitungan laju inflasi. Untuk membantu menyelesaikan pekerjaan ini, dibutuhkan metode prediksi terbaik yang mampu memprediksi pergerakan harga komoditas pangan strategis. Penelitian ini memiliki tujuan untuk menemukan model prediksi terbaik di antara Long-Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU), dalam memprediksi harga harian sepuluh komoditas pangan strategis: bawang merah, bawang putih, beras, cabai merah, cabai rawit, daging ayam, daging sapi, gula pasir, minyak goreng, dan telur ayam. Model ARIMA digunakan sebagai standar model klasik dalam penelitian kali ini. Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE), GRU memberikan hasil prediksi harga harian paling baik pada enam dari total sepuluh komoditas dan LSTM memberikan hasil prediksi terbaik pada empat komoditas sisanya. Model terbaik pada tiap komoditas berhasil mengurangi angka MAE dari ARIMA sekitar 3% hingga 43%. Ketika model mempelajari data, GRU berhasil menyelesaikan prosesnya lebih cepat daripada LSTM pada delapan komoditas. Model peramalan terbaik yang ditemukan pada penelitian kali ini dapat digunakan untuk memperbaiki metode peramalan klasik yang telah digunakan dalam memprediksi harga harian pangan Indonesia, sehingga dapat membantu pemerintah dalam memformulasikan kebijakan dan peraturan terkait manajemen stabilitas harga pangan.

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Managing strategic commodities prices in the market is considered an important task since they have a significant contribution to the calculation of the inflation rate. To aid this task, it is necessary to find the best forecasting model that can predict commodities daily price. This paper aims to find the best prediction model between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in forecasting the daily price of ten Indonesia’s strategic commodities: shallot, garlic, rice, chili pepper, cayenne pepper, broiler meat, topside beef, granulated sugar, cooking oil, chicken egg. This research used ARIMA as a benchmark model. Based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), GRU gave the best result in predicting the daily price of six out of ten commodities. It is found that the best model for each commodity managed to reduce the MAE score from ARIMA by around 3% until 43%. GRU managed to finish faster than LSTM in training eight commodities data. The best forecasting method found in this research can be used to improve the classic method to forecast the daily price of Indonesia’s food commodities in assisting the government in formulating policies and regulations related to food price management."

"ABSTRAKDalam menjaga harga pangan yang stabil, pemerintah telah menerapkan berbagai program untuk meminimalkan fluktuasi harga pangan di pasar. Dalam hal ini, Rumah Pangan Kita RPK adalah salah satu bentuk program pemerintah yang menyediakan bahan makanan pokok agar terjangkau oleh masyarakat, baik dari ketersediaan dan kepastian harga. Pengukuran menggunakan pendekatan Data Envelopment Analysis DEA digunakan untuk menentukan hasil kinerja RPK dan kemudian dikombinasikan dengan simulasi yang diterapkan dalam membandingkan pengaruh kinerja RPK terhadap stabilitas harga pangan. Salah satu rekomendasi berdasarkan pada matriks keterkaitan, dapat dilakukan penyesuaian terhadap komoditas yang disediakan seperti pada cluster beras 1 yang terkait dengan luas wilayah 0-40 km persegi dan dengan jumlah penduduk 55.000-110.000 jiwa, dimana tingkat keterkaitan dari variable tersebut mencapai 100 .

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ABSTRACTIn maintaining stable food prices, the government has implemented various programs to minimize food price fluctuations in the market. In this case, Rumah Pangan Kita RPK is one form of the government program that provides staple food to be affordable by the community, both from affordability and price certainty. Measurements using the Data Envelopment Analysis DEA approach are used to determine the performance results of RPK unit and then combined with the simulations applied in comparing the effect of RPK performance on food price stability. Based on the linkage matrix, it can be adjusted to the commodities provided as in the rice clusters 1 related to the area of 0 40 square km and with the population of 55,000 110,000 inhabitants, where the confidence level of the variable reaches 100 ."

"Penelitian ini bertujuan untuk melihat hubungan antara pelaksanaan program revitalisasi pasar dengan stabilisasi harga komoditas pangan. Tahun 2015, pemerintah memulai program Revitalisasi/Pembangunan Baru Pasar Rakyat. Salah satu prinsip revitalisasi dalam hal ekonomi sebagai instrumen menstabilkan harga bahan kebutuhan pokok (pangan) yang beredar di masyarakat. Pelaksanaan revitalisasi pasar rakyat berbeda-beda di tiap daerah di Indonesia. Oleh karena itu, perlu melihat berapa anggaran yang dikeluarkan di daerah tersebut, kepadatan penduduk, total pasar serta pasar yang direvitalisasi dan juga pendapatan per kapita. Penelitian ini menggunakan data harga 10 komoditas dari Pusat Informasi Harga Pangan Strategis (PIHPS) Nasional dan anggaran revitalisasi pasar yang diperoleh dari Kementerian Perdagangan. Menggunakan metode data panel dari pertumbuhan perubahan harga, koefisien variasi harga, dan rata-rata harga pada tingkat level dari 10 komoditi pangan per bulan dari tahun 2016-2019 dari 95 kabupaten/kota di Indonesia. Di estimasi dengan model Random Effect. Studi ini membuktikan bahwa pelaksanaan revitalisasi/pembangunan baru pasar rakyat berkorelasi dengan stabilitas harga komoditas pangan, namun di sisi lain juga meningkatkan harga rata-rata di tingkat level.

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This study aims to look at the relationship between the implementation of the market revitalization program and the stabilization of food commodity prices. In 2015, the government started a Revitalization/New Market Development program for the Peoples Market. One of the principles of revitalization in terms of the economy as an instrument is to stabilize the prices of basic needs (food) circulating in the community. The revitalization of peoples markets varies in each region in Indonesia. Therefore, it is necessary to look at how much the budget is spent in the area, population density, total market and revitalized market and income per capita. This study uses 10 commodity price data from the National Strategic Food Price Information Center (PIHPS) and market revitalization budget obtained from the Ministry of Trade. Using the panel data method of growth in price changes, coefficient of price variation, and average prices at the level of 10 food commodities per month from 2016-2019 from 95 districts/cities in Indonesia. Estimated by the Random Effect model. This study proves that the implementation of revitalization/new development of peoples markets correlated with the stability of food commodity prices, but on the other hand also increased average prices at the level."

"Fungsi utama pasar berjangka adalah sebagai sarana hedging dan sarana pembentukan harga (price discovery). Efektifnya fungsi pasar berjangka sebagai sarana hedging hanya dapat tercapai apabila fungsi price discovery dan tingkat konvergensi harga yang terjadi di pasar berjangka dapat berjalan dengan efektif.Berdasarkan hal diatas, maka tujuan penelitian ini adalah melakukan pengujian dinamika cash price dan cash equivalent price yang selanjutnya menjadi parameter terbentuknya price discovery dan tingkat konvergensi harga di pasar berjangka. Data penelitian yang digunakan adalah data transaksi harian komoditas emas dengan kode perdagangan GOL yang diperdagangkan di PT. JFX.Periode penelitian adalah 2 Januari 2007 sampai dengan 21 November 2011. Metode penelitian yang digunakan adalah model penelitian VAR/VECM. Penelitian ini memberikan bukti bahwa pasar berjangka dapat melakukan fungsi price discovery dengan baik. Namun, tingkat konvergensi harga di pasar berjangka terjadi dengan sangat lambat (tidak efektif). Hasil penelitian ini mengindikasikan tidak efektifnya kontrak berjangka komoditas emas digunakan sebagai sarana hedging/lindung nilai.

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The main functions of a future market are used as hedging facility and price discoverer. The effectiveness of future market as a hedging facility can be achived if only the function of future market as a price discoverer and the convergence rate in future market can be operating effectively.

From this problem, the intention of this studying is examining the dynamics of cash price and cash equivalent price which afterwards become parameter of price discovery process and convergence rate in future market. The research's data that used in this studying is the daily transaction data of gold future contract which have trading code as GOL traded in PT. JFX.

Research period consist from January 2nd 2007 until November 21st 2011. The method that used in this studying is VAR/VECM. This studying suggests that futures market in gold commodity can perform price discovery well. But the convergence rate in this future contract is very slow (ineffective). This result indicates that gold future contract in PT. JFX is a ineffective hedging facility."